{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ora/anaconda3/envs/tensorflow/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ora/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use the retry module or similar alternatives.\n"
     ]
    }
   ],
   "source": [
    "#import important packages/libraries\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import csv\n",
    "from sklearn.utils import shuffle\n",
    "from tensorflow.contrib.layers import flatten\n",
    "from skimage import transform as transf\n",
    "from sklearn.model_selection import train_test_split\n",
    "import cv2\n",
    "from prettytable import PrettyTable\n",
    "%matplotlib inline\n",
    "SEED = 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入数据并可视化\n",
    "training_file = 'data/train.p'\n",
    "testing_file = 'data/test.p'\n",
    "\n",
    "with open(training_file,mode='rb') as f:\n",
    "    train = pickle.load(f)\n",
    "with open(testing_file,mode='rb') as f:\n",
    "    test = pickle.load(f)\n",
    "\n",
    "X_train,y_train = train['features'],train['labels']\n",
    "X_test,y_test = test['features'],test['labels']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset Summary and Expoloration\n",
    "下面我们对德国交通指示牌数据集进行可视化处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of classes:43\n",
      "Number of training examples = 34799\n",
      "Number of testing examples = 12630\n",
      "Image data shape= (32, 32, 3)\n",
      "Number of classes = 43\n"
     ]
    }
   ],
   "source": [
    "n_train = len(X_train)\n",
    "n_test = len(X_test)\n",
    "\n",
    "_,IMG_HEIGHT,IMG_WIDTH,IMG_DEPTH = X_train.shape\n",
    "image_shape = (IMG_HEIGHT,IMG_WIDTH,IMG_DEPTH)\n",
    "\n",
    "with open('data/signnames.csv','r') as sign_name:\n",
    "    reader = csv.reader(sign_name)\n",
    "    sign_names = list(reader)\n",
    "\n",
    "sign_names = sign_names[1::]\n",
    "NUM_CLASSES = len(sign_names)\n",
    "print('Total number of classes:{}'.format(NUM_CLASSES))\n",
    "\n",
    "n_classes = len(np.unique(y_train))\n",
    "assert (NUM_CLASSES== n_classes) ,'1 or more class(es) not represented in training set'\n",
    "\n",
    "n_test = len(y_test)\n",
    "\n",
    "print('Number of training examples =',n_train)\n",
    "print('Number of testing examples =',n_test)\n",
    "print('Image data shape=',image_shape)\n",
    "print('Number of classes =',n_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data visualization,show 20 images\n",
    "def visualize_random_images(list_imgs,X_dataset,y_dataset):\n",
    "    #list_imgs:20 index\n",
    "    _,ax = plt.subplots(len(list_imgs)//5,5,figsize=(20,10))\n",
    "    row,col = 0,0\n",
    "    for idx in list_imgs:\n",
    "        img = X_dataset[idx]\n",
    "        ax[row,col].imshow(img)\n",
    "        ax[row,col].annotate(int(y_dataset[idx]),xy=(2,5),color='red',fontsize='20')\n",
    "        ax[row,col].axis('off')\n",
    "        col+=1\n",
    "        if col==5:\n",
    "            row,col = row+1,0\n",
    "    plt.show()\n",
    "ls = [random.randint(0,len(y_train)) for i in range(20)]\n",
    "visualize_random_images(ls,X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------- \n",
      "Highest count: 2010.0 (class 2)\n",
      "Lowest count: 180.0 (class 0)\n",
      "------- \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+----------------------------------------------------+\n",
      "| class value |                Name of Traffic sign                |\n",
      "+-------------+----------------------------------------------------+\n",
      "|      0      |                Speed limit (20km/h)                |\n",
      "|      1      |                Speed limit (30km/h)                |\n",
      "|      2      |                Speed limit (50km/h)                |\n",
      "|      3      |                Speed limit (60km/h)                |\n",
      "|      4      |                Speed limit (70km/h)                |\n",
      "|      5      |                Speed limit (80km/h)                |\n",
      "|      6      |            End of speed limit (80km/h)             |\n",
      "|      7      |               Speed limit (100km/h)                |\n",
      "|      8      |               Speed limit (120km/h)                |\n",
      "|      9      |                     No passing                     |\n",
      "|      10     |    No passing for vechiles over 3.5 metric tons    |\n",
      "|      11     |       Right-of-way at the next intersection        |\n",
      "|      12     |                   Priority road                    |\n",
      "|      13     |                       Yield                        |\n",
      "|      14     |                        Stop                        |\n",
      "|      15     |                    No vechiles                     |\n",
      "|      16     |      Vechiles over 3.5 metric tons prohibited      |\n",
      "|      17     |                      No entry                      |\n",
      "|      18     |                  General caution                   |\n",
      "|      19     |            Dangerous curve to the left             |\n",
      "|      20     |            Dangerous curve to the right            |\n",
      "|      21     |                    Double curve                    |\n",
      "|      22     |                     Bumpy road                     |\n",
      "|      23     |                   Slippery road                    |\n",
      "|      24     |             Road narrows on the right              |\n",
      "|      25     |                     Road work                      |\n",
      "|      26     |                  Traffic signals                   |\n",
      "|      27     |                    Pedestrians                     |\n",
      "|      28     |                 Children crossing                  |\n",
      "|      29     |                 Bicycles crossing                  |\n",
      "|      30     |                 Beware of ice/snow                 |\n",
      "|      31     |               Wild animals crossing                |\n",
      "|      32     |        End of all speed and passing limits         |\n",
      "|      33     |                  Turn right ahead                  |\n",
      "|      34     |                  Turn left ahead                   |\n",
      "|      35     |                     Ahead only                     |\n",
      "|      36     |                Go straight or right                |\n",
      "|      37     |                Go straight or left                 |\n",
      "|      38     |                     Keep right                     |\n",
      "|      39     |                     Keep left                      |\n",
      "|      40     |                Roundabout mandatory                |\n",
      "|      41     |                 End of no passing                  |\n",
      "|      42     | End of no passing by vechiles over 3.5 metric tons |\n",
      "+-------------+----------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "def get_count_imgs_per_class(y, verbose=False):\n",
    "    num_classes = len(np.unique(y))\n",
    "    count_imgs_per_class = np.zeros( num_classes )\n",
    "\n",
    "    for this_class in range( num_classes ):\n",
    "        if verbose: \n",
    "            print('class {} | count {}'.format(this_class, np.sum( y  == this_class )) )\n",
    "        count_imgs_per_class[this_class] = np.sum(y == this_class )\n",
    "    #sanity check\n",
    "    return count_imgs_per_class\n",
    "class_freq = get_count_imgs_per_class(y_train)\n",
    "print('------- ')\n",
    "print('Highest count: {} (class {})'.format(np.max(class_freq), np.argmax(class_freq)))\n",
    "print('Lowest count: {} (class {})'.format(np.min(class_freq), np.argmin(class_freq)))\n",
    "print('------- ')\n",
    "plt.bar(np.arange(NUM_CLASSES), class_freq , align='center')\n",
    "plt.xlabel('class')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlim([-1, 43])\n",
    "plt.title(\"class frequency in Training set\")\n",
    "plt.show()\n",
    "sign_name_table = PrettyTable()\n",
    "sign_name_table.field_names = ['class value', 'Name of Traffic sign']\n",
    "for i in range(len(sign_names)):\n",
    "    sign_name_table.add_row([sign_names[i][0], sign_names[i][1]] )\n",
    "    \n",
    "print(sign_name_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram of selected images from the class38 ......\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def histograms_randImgs(label,channel,n_imgs=5,ylim=50):\n",
    "    '''\n",
    "    Histogram (pixel intensity distribution) for a selection of images with the same label.\n",
    "    For better visualization, the images are shown in grayscale\n",
    "    label - the label of the images\n",
    "    n_imgs - number of images to show (default=5)\n",
    "    channel - channel used to compute histogram\n",
    "    ylim - range of y axis values for histogram plot (default=50)\n",
    "    '''\n",
    "    assert channel < 3,'image are RGB,choose channel value between in the range[0,2]'\n",
    "    assert (np.sum(y_train==label))>=n_imgs,'reduce your number of images'\n",
    "    \n",
    "    all_imgs = np.ravel(np.argwhere(y_train==label))\n",
    "    \n",
    "    #随机选择5张图片\n",
    "    ls_idx = np.random.choice(all_imgs,size=n_imgs,replace=False)\n",
    "    _,ax = plt.subplots(n_imgs,2,figsize=(10,10))\n",
    "    print('Histogram of selected images from the class{} ......'.format(label))\n",
    "    row,col = 0,0\n",
    "    for idx in ls_idx:\n",
    "        img = X_train[idx,:,:,channel]\n",
    "        #print(img.shape)\n",
    "        ax[row,col].imshow(img,cmap='gray')\n",
    "        ax[row,col].axis('off')\n",
    "        \n",
    "        hist = np.histogram(img,bins=256)\n",
    "        ax[row,col+1].hist(hist,bins=256)\n",
    "        ax[row,col+1].set_xlim([0,100])\n",
    "        ax[row,col+1].set_ylim([0,ylim])\n",
    "        col,row = 0,row+1\n",
    "    plt.show()\n",
    "histograms_randImgs(38,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 接下来对数据做进一步处理\n",
    "我们完成以下几个步骤：\n",
    ">* 数据增强\n",
    ">* 将RGB转换成Grayscale\n",
    ">* 数据尺度变换\n",
    "\n",
    "**Note**：数据集的划分必须在数据增强完成前(防止验证集被合成图像污染)\n",
    "## 数据增强具体步骤\n",
    "这里的数据增强主要是：1.增加训练集的大小 2.调整了类别分布（类别分布是不均衡的，因为测试集可能相较与训练集来讲，有着不同的分布，因此我们希望在类别分布均衡的数据集上训练，给不同类别相同的权重，然后在不均衡的数据集上测试时可以有更好的效果）\n",
    "数据增强后，我们得到每个类别4000张图片\n",
    "数据增强的方法主要就是从原始数据集中随机选取图片，并应用仿射变换\n",
    ">* 旋转角度我限制在【-10，10】度之间，如果旋转角度过大，有些交通标志的意思可能就会发生变化了\n",
    ">* 水平、垂直移动的话，范围限制在【-3，3】px之间\n",
    ">* 伸缩变换限制在【0.8，1.2】"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set size:27839|Validation set size:6960\n",
      "\n",
      "start data augmentation.....\n",
      "\t Class 0|Number of extra imgs3855\n",
      "\t Class 1|Number of extra imgs2407\n",
      "\t Class 2|Number of extra imgs2411\n",
      "\t Class 3|Number of extra imgs2985\n",
      "\t Class 4|Number of extra imgs2577\n",
      "\t Class 5|Number of extra imgs2677\n",
      "\t Class 6|Number of extra imgs3715\n",
      "\t Class 7|Number of extra imgs2965\n",
      "\t Class 8|Number of extra imgs2987\n",
      "\t Class 9|Number of extra imgs2953\n",
      "\t Class 10|Number of extra imgs2570\n",
      "\t Class 11|Number of extra imgs3047\n",
      "\t Class 12|Number of extra imgs2481\n",
      "\t Class 13|Number of extra imgs2477\n",
      "\t Class 14|Number of extra imgs3444\n",
      "\t Class 15|Number of extra imgs3572\n",
      "\t Class 16|Number of extra imgs3711\n",
      "\t Class 17|Number of extra imgs3206\n",
      "\t Class 18|Number of extra imgs3163\n",
      "\t Class 19|Number of extra imgs3861\n",
      "\t Class 20|Number of extra imgs3770\n",
      "\t Class 21|Number of extra imgs3786\n",
      "\t Class 22|Number of extra imgs3739\n",
      "\t Class 23|Number of extra imgs3631\n",
      "\t Class 24|Number of extra imgs3800\n",
      "\t Class 25|Number of extra imgs2922\n",
      "\t Class 26|Number of extra imgs3566\n",
      "\t Class 27|Number of extra imgs3828\n",
      "\t Class 28|Number of extra imgs3615\n",
      "\t Class 29|Number of extra imgs3812\n",
      "\t Class 30|Number of extra imgs3684\n",
      "\t Class 31|Number of extra imgs3453\n",
      "\t Class 32|Number of extra imgs3850\n",
      "\t Class 33|Number of extra imgs3511\n",
      "\t Class 34|Number of extra imgs3704\n",
      "\t Class 35|Number of extra imgs3132\n",
      "\t Class 36|Number of extra imgs3733\n",
      "\t Class 37|Number of extra imgs3853\n",
      "\t Class 38|Number of extra imgs2518\n",
      "\t Class 39|Number of extra imgs3783\n",
      "\t Class 40|Number of extra imgs3753\n",
      "\t Class 41|Number of extra imgs3828\n",
      "\t Class 42|Number of extra imgs3826\n"
     ]
    }
   ],
   "source": [
    "def random_transform(img,angle_range=[-10,10],\n",
    "                    scale_range=[0.8,1.2],\n",
    "                    translation_range=[-3,3]):\n",
    "    '''\n",
    "    The function takes an image and performs a set of random affine transformation.\n",
    "    img:original images\n",
    "    ang_range:angular range of the rotation [-15,+15] deg for example\n",
    "    scale_range: [0.8,1.2]\n",
    "    shear_range:[10,-10]\n",
    "    translation_range:[-2,2]\n",
    "    '''\n",
    "    img_height,img_width,img_depth = img.shape\n",
    "    # Generate random parameter values\n",
    "    angle_value = np.random.uniform(low=angle_range[0],high=angle_range[1],size=None)\n",
    "    scaleX = np.random.uniform(low=scale_range[0],high=scale_range[1],size=None)\n",
    "    scaleY = np.random.uniform(low=scale_range[0],high=scale_range[1],size=None)\n",
    "    translationX = np.random.randint(low=translation_range[0],high=translation_range[1]+1,size=None)\n",
    "    translationY = np.random.randint(low=translation_range[0],high=translation_range[1]+1,size=None)\n",
    "    \n",
    "    center_shift = np.array([img_height,img_width])/2. - 0.5\n",
    "    transform_center = transf.SimilarityTransform(translation=-center_shift)\n",
    "    transform_uncenter = transf.SimilarityTransform(translation=center_shift)\n",
    "    \n",
    "    transform_aug = transf.AffineTransform(rotation=np.deg2rad(angle_value),\n",
    "                                          scale=(1/scaleY,1/scaleX),\n",
    "                                          translation = (translationY,translationX))\n",
    "    #Image transformation : includes rotation ,shear,translation,zoom\n",
    "    full_tranform = transform_center + transform_aug + transform_uncenter\n",
    "    new_img = transf.warp(img,full_tranform,preserve_range=True)\n",
    "    \n",
    "    return new_img.astype('uint8')\n",
    "\n",
    "def data_augmentation(X_dataset,y_dataset,augm_nbr,keep_dist=True):\n",
    "    '''\n",
    "    X_dataset:image dataset to augment\n",
    "    y_dataset:label dataset\n",
    "    keep_dist - True:keep class distribution of original dataset,\n",
    "                False:balance dataset\n",
    "    augm_param - is the augmentation parameter\n",
    "                if keep_dist is True,increase the dataset by the factor 'augm_nbr' (2x,5x or 10x...)\n",
    "                if keep_dist is False,make all classes have same number of images:'augm_nbr'(2500,3000 or 4000 imgs)\n",
    "    '''\n",
    "    X_train_dtype = X_train\n",
    "    n_classes = len(np.unique(y_dataset))\n",
    "    _,img_height,img_width,img_depth = X_dataset.shape\n",
    "    class_freq = get_count_imgs_per_class(y_train)\n",
    "    \n",
    "    if keep_dist:\n",
    "        extra_imgs_per_class = np.array([augm_nbr*x for x in get_count_imgs_per_class(y_dataset)])\n",
    "    else:\n",
    "        assert (augm_nbr>np.argmax(class_freq)),'augm_nbr must be larger than the height class count'\n",
    "        extra_imgs_per_class = augm_nbr - get_count_imgs_per_class(y_dataset)\n",
    "    \n",
    "    total_extra_imgs = np.sum(extra_imgs_per_class)\n",
    "    \n",
    "    #if extra data is needed->run the dataaumentation op\n",
    "    if total_extra_imgs > 0:\n",
    "        X_extra = np.zeros((int(total_extra_imgs),img_height,img_width,img_depth),dtype=X_train.dtype)\n",
    "        y_extra = np.zeros(int(total_extra_imgs))\n",
    "        start_idx = 0\n",
    "        print('start data augmentation.....')\n",
    "        for this_class in range(n_classes):\n",
    "            print('\\t Class {}|Number of extra imgs{}'.format(this_class,int(extra_imgs_per_class[this_class])))\n",
    "            n_extra_imgs = extra_imgs_per_class[this_class]\n",
    "            end_idx = start_idx + n_extra_imgs\n",
    "            \n",
    "            if n_extra_imgs > 0:\n",
    "                #get ids of all images belonging to this_class\n",
    "                all_imgs_id = np.argwhere(y_dataset==this_class)\n",
    "                new_imgs_x = np.zeros((int(n_extra_imgs),img_height,img_width,img_depth))\n",
    "                \n",
    "                for k in range(int(n_extra_imgs)):\n",
    "                    #randomly pick an original image belonging to this class\n",
    "                    rand_id = np.random.choice(all_imgs_id[0],size=None,replace=True)\n",
    "                    rand_img = X_train[rand_id]\n",
    "                    #Transform image\n",
    "                    new_img = random_transform(rand_img)\n",
    "                    new_imgs_x[k,:,:,:] = new_img\n",
    "                #update tensors with new images and associated labels\n",
    "                X_extra[int(start_idx):int(end_idx)] = new_imgs_x\n",
    "                y_extra[int(start_idx):int(end_idx)] = np.ones((int(n_extra_imgs),))*this_class\n",
    "                start_idx = end_idx\n",
    "        return [X_extra,y_extra]\n",
    "    else:\n",
    "        return [None,None]\n",
    "# shuffle train dataset before split\n",
    "X_train,y_train = shuffle(X_train,y_train)\n",
    "_,IMG_HEIGHT,IMG_WIDTH,IMG_DEPTH = X_train.shape\n",
    "\n",
    "X_train,X_validation,y_train,y_validation = train_test_split(X_train,y_train,test_size=0.2,random_state=SEED)\n",
    "print('Train set size:{}|Validation set size:{}\\n'.format(X_train.shape[0],X_validation.shape[0]))\n",
    "\n",
    "X_extra,y_extra = data_augmentation(X_train,y_train,augm_nbr=4000,keep_dist=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize 20 examples picked randomly from train dataset\n",
    "ls = [random.randint(0,len(y_extra)) for i in range(20)]\n",
    "visualize_random_images(list_imgs=ls,X_dataset=X_extra,y_dataset=y_extra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "if X_extra is not None:\n",
    "    X_train = np.concatenate((X_train,X_extra.astype('uint8')),axis=0)\n",
    "    y_train = np.concatenate((y_train,y_extra),axis=0)\n",
    "    del X_extra,y_extra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# visualization after data augmentation\n",
    ">* Display 20 random images\n",
    ">* show frequency of each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Train dataset after augmentation\n",
      "\t Total Number of images in Train dataset:172000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Validation dataset\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls = [random.randint(0,len(y_train)) for i in range(20)]\n",
    "visualize_random_images(list_imgs=ls,X_dataset=X_train,y_dataset=y_train)\n",
    "\n",
    "print('*** Train dataset after augmentation')\n",
    "print('\\t Total Number of images in Train dataset:{}'.format(X_train.shape[0]))\n",
    "\n",
    "plt.bar(np.arange(n_classes),get_count_imgs_per_class(y_train),align='center')\n",
    "plt.xlabel('class')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlim([-1,43])\n",
    "plt.show()\n",
    "\n",
    "print('*** Validation dataset')\n",
    "plt.bar(np.arange(n_classes),get_count_imgs_per_class(y_validation),align='center')\n",
    "plt.xlabel('class')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlim([-1,43])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessed(dataset):\n",
    "    n_imgs,img_height,img_width,_ = dataset.shape\n",
    "    processed_dataset = np.zeros((n_imgs,img_height,img_width,1))\n",
    "    for idx in range(len(dataset)):\n",
    "        img = dataset[idx]\n",
    "        gray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)\n",
    "        \n",
    "        processed_dataset[idx,:,:,0] = gray/255. - 0.5\n",
    "    return processed_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 设计并测试模型架构\n",
    "接下来就是我们的重头戏了。\n",
    "我们需要设计并实现一个深度学习模型并用来学习识别交通信号。\n",
    "在这个过程中，我们需要思考并考虑以下几点内容：\n",
    ">* 神经网络架构\n",
    ">* 如何应用前面实现好的预处理技术\n",
    ">* 样本不均匀带来的问题\n",
    ">* 生成虚假数据\n",
    "具体的设计思路，大家可以参考LeCun于2011年发表的文章：[Traffic Sign Recognition with Multi-Scale Convolutional Networks](https://scholar.google.com/scholar_url?url=http://ieeexplore.ieee.org/abstract/document/6033589/&hl=zh-CN&sa=T&oi=gsb&ct=res&cd=0&ei=3rrqWtr1IsSyyASpkrLgDA&scisig=AAGBfm1qTtsdXG41K740eWaSoOr0-BKhoQ)\n",
    "\n",
    "这里我们设计一个简单的卷积神经网络，它由两大部分组成：\n",
    "**1:**卷积层\n",
    "**2:**全连接层\n",
    "具体架构图如下所示：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variables Initialization function and Operation\n",
    "def weight_variable(shape,mean,stddev,name,seed=SEED):\n",
    "    init = tf.truncated_normal(shape,mean=mean,stddev=stddev,seed=SEED)\n",
    "    return tf.Variable(init,name=name)\n",
    "\n",
    "def bias_variable(shape,init_value,name):\n",
    "    init = tf.constant(init_value,shape=shape)\n",
    "    return tf.Variable(init,name=name)\n",
    "\n",
    "def conv2d(x,W,strides,padding,name):\n",
    "    return tf.nn.conv2d(x,W,strides=strides,padding=padding,name=name)\n",
    "\n",
    "def max_2x2_pool(x,padding,name):\n",
    "    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding=padding,name=name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#weights and biases\n",
    "#parameters\n",
    "\n",
    "IMG_DEPTH = 1\n",
    "mu =0\n",
    "sigma = 0.05\n",
    "bias_init = 0.05\n",
    "\n",
    "weights ={  \n",
    "    'W_conv1': weight_variable([3, 3, IMG_DEPTH, 80], mean=mu, stddev=sigma, name='W_conv1'),\n",
    "    'W_conv2': weight_variable([3, 3, 80, 120], mean=mu, stddev=sigma, name='W_conv2'),\n",
    "    'W_conv3': weight_variable([4, 4, 120, 180], mean=mu, stddev=sigma, name='W_conv3'),\n",
    "    'W_conv4': weight_variable([3, 3, 180, 200], mean=mu, stddev=sigma, name='W_conv4'),\n",
    "    'W_conv5': weight_variable([3, 3, 200, 200], mean=mu, stddev=sigma, name='W_conv5'),\n",
    "    'W_fc1': weight_variable([800, 80], mean=mu, stddev=sigma, name='W_fc1'),\n",
    "    'W_fc2': weight_variable([80, 80], mean=mu, stddev=sigma, name='W_fc2'),\n",
    "    'W_fc3': weight_variable([80, 43], mean=mu, stddev=sigma, name='W_fc3'),\n",
    "}\n",
    "biases = {\n",
    "    'b_conv1': bias_variable(shape=[80], init_value=bias_init, name='b_conv1'),\n",
    "    'b_conv2': bias_variable(shape=[120], init_value=bias_init, name='b_conv2'),\n",
    "    'b_conv3': bias_variable(shape=[180], init_value=bias_init, name='b_conv3'),\n",
    "    'b_conv4': bias_variable(shape=[200], init_value=bias_init, name='b_conv4'),\n",
    "    'b_conv5': bias_variable(shape=[200], init_value=bias_init, name='b_conv5'),\n",
    "    'b_fc1': bias_variable([80], init_value=bias_init, name='b_fc1'),\n",
    "    'b_fc2': bias_variable([80], init_value=bias_init, name='b_fc2'),\n",
    "    'b_fc3': bias_variable([43], init_value=bias_init, name='b_fc3'),\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def traffic_model(x,keep_prob,keep_p_conv,weights,biases):\n",
    "    '''\n",
    "    ConvNet model for Traffic sign classifier\n",
    "    x - input image is tensor of shape(n_imgs,img_height,img_width,img_depth)\n",
    "    keep_prob - hyper parameter of the dropout operation\n",
    "    weights - dictionary of the weights for convolution layers and fully connected layers\n",
    "    biases dictionary of the biases for convolutional layers and fully connected layers\n",
    "    '''\n",
    "    # Convolutional block 1\n",
    "    conv1 = conv2d(x, weights['W_conv1'], strides=[1,1,1,1], padding='VALID', name='conv1_op')\n",
    "    conv1_act = tf.nn.relu(conv1 + biases['b_conv1'], name='conv1_act')\n",
    "    conv1_drop = tf.nn.dropout(conv1_act, keep_prob=k_p_conv, name='conv1_drop')\n",
    "    conv2 = conv2d(conv1_drop, weights['W_conv2'], strides=[1,1,1,1], padding='SAME', name='conv2_op')\n",
    "    conv2_act = tf.nn.relu(conv2 + biases['b_conv2'], name='conv2_act')\n",
    "    conv2_pool = max_2x2_pool(conv2_act, padding='VALID', name='conv2_pool')\n",
    "    pool2_drop = tf.nn.dropout(conv2_pool, keep_prob=k_p_conv, name='conv2_drop')\n",
    "    \n",
    "    #Convolution block 2\n",
    "    conv3 = conv2d(pool2_drop, weights['W_conv3'], strides=[1,1,1,1], padding='VALID', name='conv3_op')\n",
    "    conv3_act = tf.nn.relu(conv3 + biases['b_conv3'], name='conv3_act')\n",
    "    conv3_drop = tf.nn.dropout(conv3_act, keep_prob=k_p_conv, name='conv3_drop')\n",
    "    conv4 = conv2d(conv3_drop, weights['W_conv4'], strides=[1,1,1,1], padding='SAME', name='conv4_op')\n",
    "    conv4_act = tf.nn.relu(conv4 + biases['b_conv4'], name='conv4_act')\n",
    "    conv4_pool = max_2x2_pool(conv4_act, padding='VALID', name='conv4_pool')\n",
    "    conv4_drop = tf.nn.dropout(conv4_pool, keep_prob, name='conv4_drop')\n",
    "    \n",
    "    conv5 = conv2d(conv4_drop, weights['W_conv5'], strides=[1,1,1,1], padding='VALID', name='conv5_op')\n",
    "    conv5_act = tf.nn.relu(conv5 + biases['b_conv5'], name='conv5_act')\n",
    "    conv5_pool = max_2x2_pool(conv5_act, padding='VALID', name='conv5_pool')\n",
    "    conv5_drop = tf.nn.dropout(conv5_pool, keep_prob, name='conv5_drop')\n",
    "    \n",
    "    #Fully connected layers\n",
    "    fc0 = flatten(conv5_drop)\n",
    "    fc1 = tf.nn.relu( tf.matmul( fc0, weights['W_fc1'] ) + biases['b_fc1'], name='fc1' )\n",
    "    fc1_drop = tf.nn.dropout(fc1, keep_prob, name='fc1_drop')\n",
    "    fc2 = tf.nn.relu( tf.matmul( fc1_drop, weights['W_fc2'] ) + biases['b_fc2'], name='fc2' )\n",
    "    fc2_drop = tf.nn.dropout(fc2, keep_prob, name='fc2_drop')\n",
    "    logits = tf.add(tf.matmul(fc2_drop, weights['W_fc3']),biases['b_fc3'], name='logits')  \n",
    "    \n",
    "    return [weights, logits]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train your model here\n",
    "x = tf.placeholder(tf.float32,(None,IMG_HEIGHT,IMG_WIDTH,IMG_DEPTH),name='x')\n",
    "y = tf.placeholder(tf.int32,(None),name='y')\n",
    "keep_prob = tf.placeholder(tf.float32,name='keep_prob')\n",
    "k_p_conv = tf.placeholder(tf.float32,name='k_p_conv')\n",
    "one_hot_y = tf.one_hot(y,n_classes)\n",
    "rate = tf.placeholder(tf.float32,name='rate')\n",
    "\n",
    "weights,logits = traffic_model(x,keep_prob,k_p_conv,weights,biases)\n",
    "softmax_operation = tf.nn.softmax(logits)\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits = logits,labels = one_hot_y)\n",
    "beta = 0.0001\n",
    "loss_reg = beta*(tf.nn.l2_loss(weights['W_fc1'])+tf.nn.l2_loss(weights['W_fc2'])+tf.nn.l2_loss(weights['W_fc3']))\n",
    "loss = tf.reduce_mean(cross_entropy)+loss_reg\n",
    "\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=rate)\n",
    "training_operation = optimizer.minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct_prediction = tf.equal(tf.argmax(logits,1),tf.argmax(one_hot_y,1))\n",
    "accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))\n",
    "\n",
    "def evaluate(X_data,y_data):\n",
    "    num_examples = len(X_data)\n",
    "    total_accuracy = 0\n",
    "    sess = tf.get_default_session()\n",
    "    total_l = 0\n",
    "    for offset in range(0,num_examples,BATCH_SIZE):\n",
    "        batch_x,batch_y = X_data[offset:offset+BATCH_SIZE],y_data[offset:offset+BATCH_SIZE]\n",
    "        accuracy,l = sess.run([accuracy_operation,loss],feed_dict={x:batch_x,y:batch_y,k_p_conv:1,keep_prob:1})\n",
    "        total_accuracy+=(accuracy*len(batch_x))\n",
    "        total_l +=l*len(batch_x)\n",
    "    return [total_accuracy/num_examples,total_l/num_examples]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "histogram equalzier turn off\n",
    "EPOCHs=100\n",
    "l_rate decreases from 0.001 to l_rate/5 at 30 EPOCH and 50 EPOCHS\n",
    "Keep same class distribution as original dataset:augmentation=6X\n",
    "No keep prob for conv\n",
    "'''\n",
    "EPOCHS = 150\n",
    "BATCH_SIZE = 200\n",
    "model_nbr = 'ora'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training... \n",
      "\n",
      "Pre-processing X_train...\n",
      "X_train preprocessed dataset size:(172000, 32, 32, 1)|data type:float64\n",
      "End preprocessing X_train...\n",
      "EPOCH1...\n",
      "Train accuracy:0.7694|Validation Accuracy=0.1568\n",
      "Train loss:0.85597|Validation loss = 3.20545\n",
      "\n",
      "EPOCH2...\n",
      "Train accuracy:0.8717|Validation Accuracy=0.3497\n",
      "Train loss:0.47022|Validation loss = 2.43436\n",
      "\n",
      "EPOCH3...\n",
      "Train accuracy:0.8936|Validation Accuracy=0.4412\n",
      "Train loss:0.36167|Validation loss = 1.98520\n",
      "\n",
      "EPOCH4...\n",
      "Train accuracy:0.9151|Validation Accuracy=0.5851\n",
      "Train loss:0.28355|Validation loss = 1.51956\n",
      "\n",
      "EPOCH5...\n",
      "Train accuracy:0.9366|Validation Accuracy=0.6519\n",
      "Train loss:0.23769|Validation loss = 1.22699\n",
      "\n",
      "EPOCH6...\n",
      "Train accuracy:0.9621|Validation Accuracy=0.7690\n",
      "Train loss:0.16787|Validation loss = 0.87677\n",
      "\n",
      "EPOCH7...\n",
      "Train accuracy:0.9701|Validation Accuracy=0.8065\n",
      "Train loss:0.12972|Validation loss = 0.70467\n",
      "\n",
      "EPOCH8...\n",
      "Train accuracy:0.9831|Validation Accuracy=0.8924\n",
      "Train loss:0.08752|Validation loss = 0.42629\n",
      "\n",
      "EPOCH9...\n",
      "Train accuracy:0.9880|Validation Accuracy=0.9239\n",
      "Train loss:0.07058|Validation loss = 0.33235\n",
      "\n",
      "EPOCH10...\n",
      "Train accuracy:0.9902|Validation Accuracy=0.9376\n",
      "Train loss:0.06168|Validation loss = 0.26408\n",
      "\n",
      "EPOCH11...\n",
      "Train accuracy:0.9926|Validation Accuracy=0.9509\n",
      "Train loss:0.05139|Validation loss = 0.20411\n",
      "\n",
      "EPOCH12...\n",
      "Train accuracy:0.9956|Validation Accuracy=0.9716\n",
      "Train loss:0.04205|Validation loss = 0.14317\n",
      "\n",
      "EPOCH13...\n",
      "Train accuracy:0.9950|Validation Accuracy=0.9677\n",
      "Train loss:0.04503|Validation loss = 0.14949\n",
      "\n",
      "EPOCH14...\n",
      "Train accuracy:0.9977|Validation Accuracy=0.9835\n",
      "Train loss:0.03547|Validation loss = 0.10054\n",
      "\n",
      "EPOCH15...\n",
      "Train accuracy:0.9983|Validation Accuracy=0.9861\n",
      "Train loss:0.03251|Validation loss = 0.08827\n",
      "\n",
      "EPOCH16...\n",
      "Train accuracy:0.9983|Validation Accuracy=0.9861\n",
      "Train loss:0.03222|Validation loss = 0.08992\n",
      "\n",
      "EPOCH17...\n",
      "Train accuracy:0.9986|Validation Accuracy=0.9905\n",
      "Train loss:0.03100|Validation loss = 0.07214\n",
      "\n",
      "EPOCH18...\n",
      "Train accuracy:0.9975|Validation Accuracy=0.9816\n",
      "Train loss:0.03493|Validation loss = 0.10005\n",
      "\n",
      "EPOCH19...\n",
      "Train accuracy:0.9991|Validation Accuracy=0.9930\n",
      "Train loss:0.02815|Validation loss = 0.05703\n",
      "\n",
      "EPOCH20...\n",
      "Train accuracy:0.9984|Validation Accuracy=0.9879\n",
      "Train loss:0.03045|Validation loss = 0.07564\n",
      "\n",
      "EPOCH21...\n",
      "Train accuracy:0.9990|Validation Accuracy=0.9928\n",
      "Train loss:0.02806|Validation loss = 0.05767\n",
      "\n",
      "EPOCH22...\n",
      "Train accuracy:0.9989|Validation Accuracy=0.9920\n",
      "Train loss:0.02801|Validation loss = 0.05732\n",
      "\n",
      "EPOCH23...\n",
      "Train accuracy:0.9994|Validation Accuracy=0.9941\n",
      "Train loss:0.02607|Validation loss = 0.05069\n",
      "\n",
      "EPOCH24...\n",
      "Train accuracy:0.9995|Validation Accuracy=0.9954\n",
      "Train loss:0.02559|Validation loss = 0.04484\n",
      "\n",
      "EPOCH25...\n",
      "Train accuracy:0.9995|Validation Accuracy=0.9958\n",
      "Train loss:0.02506|Validation loss = 0.04574\n",
      "\n",
      "EPOCH26...\n",
      "Train accuracy:0.9995|Validation Accuracy=0.9950\n",
      "Train loss:0.02484|Validation loss = 0.04574\n",
      "\n",
      "EPOCH27...\n",
      "Train accuracy:0.9989|Validation Accuracy=0.9899\n",
      "Train loss:0.02690|Validation loss = 0.06082\n",
      "\n",
      "EPOCH28...\n",
      "Train accuracy:0.9993|Validation Accuracy=0.9938\n",
      "Train loss:0.02516|Validation loss = 0.05327\n",
      "\n",
      "EPOCH29...\n",
      "Train accuracy:0.9992|Validation Accuracy=0.9941\n",
      "Train loss:0.02526|Validation loss = 0.04978\n",
      "\n",
      "EPOCH30...\n",
      "Train accuracy:0.9994|Validation Accuracy=0.9954\n",
      "Train loss:0.02405|Validation loss = 0.04180\n",
      "\n",
      "EPOCH31...\n",
      "Train accuracy:0.9996|Validation Accuracy=0.9958\n",
      "Train loss:0.02355|Validation loss = 0.04137\n",
      "\n",
      "EPOCH32...\n",
      "Train accuracy:0.9991|Validation Accuracy=0.9921\n",
      "Train loss:0.02480|Validation loss = 0.05153\n",
      "\n",
      "EPOCH33...\n",
      "Train accuracy:0.9980|Validation Accuracy=0.9859\n",
      "Train loss:0.03463|Validation loss = 0.08287\n",
      "\n",
      "EPOCH34...\n",
      "Train accuracy:0.9996|Validation Accuracy=0.9948\n",
      "Train loss:0.02295|Validation loss = 0.04125\n",
      "\n",
      "EPOCH35...\n",
      "Train accuracy:0.9996|Validation Accuracy=0.9957\n",
      "Train loss:0.02397|Validation loss = 0.04951\n",
      "\n",
      "EPOCH36...\n",
      "Train accuracy:0.9997|Validation Accuracy=0.9967\n",
      "Train loss:0.02230|Validation loss = 0.03821\n",
      "\n",
      "EPOCH37...\n",
      "Train accuracy:0.9997|Validation Accuracy=0.9966\n",
      "Train loss:0.02210|Validation loss = 0.03939\n",
      "\n",
      "EPOCH38...\n",
      "Train accuracy:0.9995|Validation Accuracy=0.9960\n",
      "Train loss:0.02294|Validation loss = 0.03852\n",
      "\n",
      "EPOCH39...\n",
      "Train accuracy:0.9995|Validation Accuracy=0.9951\n",
      "Train loss:0.02307|Validation loss = 0.04202\n",
      "\n",
      "EPOCH40...\n",
      "Train accuracy:0.9997|Validation Accuracy=0.9963\n",
      "Train loss:0.02181|Validation loss = 0.03631\n",
      "\n",
      "EPOCH41...\n",
      "Train accuracy:0.9998|Validation Accuracy=0.9977\n",
      "Train loss:0.02094|Validation loss = 0.03136\n",
      "\n",
      "EPOCH42...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9978\n",
      "Train loss:0.02061|Validation loss = 0.03023\n",
      "\n",
      "EPOCH43...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9978\n",
      "Train loss:0.02031|Validation loss = 0.03088\n",
      "\n",
      "EPOCH44...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9978\n",
      "Train loss:0.01991|Validation loss = 0.02943\n",
      "\n",
      "EPOCH45...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9978\n",
      "Train loss:0.01955|Validation loss = 0.03104\n",
      "\n",
      "EPOCH46...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9978\n",
      "Train loss:0.01921|Validation loss = 0.02844\n",
      "\n",
      "EPOCH47...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9977\n",
      "Train loss:0.01893|Validation loss = 0.02889\n",
      "\n",
      "EPOCH48...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9974\n",
      "Train loss:0.01851|Validation loss = 0.02856\n",
      "\n",
      "EPOCH49...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9980\n",
      "Train loss:0.01810|Validation loss = 0.02640\n",
      "\n",
      "EPOCH50...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9974\n",
      "Train loss:0.01792|Validation loss = 0.02705\n",
      "\n",
      "EPOCH51...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9980\n",
      "Train loss:0.01740|Validation loss = 0.02513\n",
      "\n",
      "EPOCH52...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9980\n",
      "Train loss:0.01706|Validation loss = 0.02469\n",
      "\n",
      "EPOCH53...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9983\n",
      "Train loss:0.01685|Validation loss = 0.02377\n",
      "\n",
      "EPOCH54...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9983\n",
      "Train loss:0.01649|Validation loss = 0.02290\n",
      "\n",
      "EPOCH55...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9984\n",
      "Train loss:0.01624|Validation loss = 0.02354\n",
      "\n",
      "EPOCH56...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9976\n",
      "Train loss:0.01609|Validation loss = 0.02447\n",
      "\n",
      "EPOCH57...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9983\n",
      "Train loss:0.01568|Validation loss = 0.02286\n",
      "\n",
      "EPOCH58...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9986\n",
      "Train loss:0.01543|Validation loss = 0.02344\n",
      "\n",
      "EPOCH59...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9984\n",
      "Train loss:0.01534|Validation loss = 0.02278\n",
      "\n",
      "EPOCH60...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9983\n",
      "Train loss:0.01504|Validation loss = 0.02128\n",
      "\n",
      "EPOCH61...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9984\n",
      "Train loss:0.01477|Validation loss = 0.02013\n",
      "\n",
      "EPOCH62...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9980\n",
      "Train loss:0.01456|Validation loss = 0.02170\n",
      "\n",
      "EPOCH63...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9981\n",
      "Train loss:0.01438|Validation loss = 0.02092\n",
      "\n",
      "EPOCH64...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9984\n",
      "Train loss:0.01415|Validation loss = 0.02071\n",
      "\n",
      "EPOCH65...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9984\n",
      "Train loss:0.01398|Validation loss = 0.02042\n",
      "\n",
      "EPOCH66...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9978\n",
      "Train loss:0.01406|Validation loss = 0.02171\n",
      "\n",
      "EPOCH67...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9984\n",
      "Train loss:0.01363|Validation loss = 0.02013\n",
      "\n",
      "EPOCH68...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9984\n",
      "Train loss:0.01345|Validation loss = 0.01918\n",
      "\n",
      "EPOCH69...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9986\n",
      "Train loss:0.01327|Validation loss = 0.01939\n",
      "\n",
      "EPOCH70...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9983\n",
      "Train loss:0.01311|Validation loss = 0.01883\n",
      "\n",
      "EPOCH71...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9987\n",
      "Train loss:0.01294|Validation loss = 0.01807\n",
      "\n",
      "EPOCH72...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9984\n",
      "Train loss:0.01277|Validation loss = 0.01916\n",
      "\n",
      "EPOCH73...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9986\n",
      "Train loss:0.01263|Validation loss = 0.01816\n",
      "\n",
      "EPOCH74...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9987\n",
      "Train loss:0.01251|Validation loss = 0.01849\n",
      "\n",
      "EPOCH75...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9987\n",
      "Train loss:0.01236|Validation loss = 0.01717\n",
      "\n",
      "EPOCH76...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9987\n",
      "Train loss:0.01219|Validation loss = 0.01685\n",
      "\n",
      "EPOCH77...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9984\n",
      "Train loss:0.01205|Validation loss = 0.01937\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH78...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9986\n",
      "Train loss:0.01199|Validation loss = 0.01767\n",
      "\n",
      "EPOCH79...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9986\n",
      "Train loss:0.01182|Validation loss = 0.01697\n",
      "\n",
      "EPOCH80...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9987\n",
      "Train loss:0.01173|Validation loss = 0.01656\n",
      "\n",
      "EPOCH81...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9984\n",
      "Train loss:0.01164|Validation loss = 0.01724\n",
      "\n",
      "EPOCH82...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9990\n",
      "Train loss:0.01146|Validation loss = 0.01578\n",
      "\n",
      "EPOCH83...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9987\n",
      "Train loss:0.01133|Validation loss = 0.01614\n",
      "\n",
      "EPOCH84...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9987\n",
      "Train loss:0.01121|Validation loss = 0.01609\n",
      "\n",
      "EPOCH85...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9986\n",
      "Train loss:0.01119|Validation loss = 0.01622\n",
      "\n",
      "EPOCH86...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9986\n",
      "Train loss:0.01105|Validation loss = 0.01661\n",
      "\n",
      "EPOCH87...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9986\n",
      "Train loss:0.01106|Validation loss = 0.01679\n",
      "\n",
      "EPOCH88...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.01083|Validation loss = 0.01558\n",
      "\n",
      "EPOCH89...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.01070|Validation loss = 0.01482\n",
      "\n",
      "EPOCH90...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9986\n",
      "Train loss:0.01061|Validation loss = 0.01672\n",
      "\n",
      "EPOCH91...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9987\n",
      "Train loss:0.01053|Validation loss = 0.01476\n",
      "\n",
      "EPOCH92...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9987\n",
      "Train loss:0.01045|Validation loss = 0.01563\n",
      "\n",
      "EPOCH93...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9986\n",
      "Train loss:0.01034|Validation loss = 0.01690\n",
      "\n",
      "EPOCH94...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9987\n",
      "Train loss:0.01026|Validation loss = 0.01525\n",
      "\n",
      "EPOCH95...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9990\n",
      "Train loss:0.01018|Validation loss = 0.01435\n",
      "\n",
      "EPOCH96...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9989\n",
      "Train loss:0.01014|Validation loss = 0.01524\n",
      "\n",
      "EPOCH97...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9987\n",
      "Train loss:0.01001|Validation loss = 0.01480\n",
      "\n",
      "EPOCH98...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9983\n",
      "Train loss:0.00992|Validation loss = 0.01594\n",
      "\n",
      "EPOCH99...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.00987|Validation loss = 0.01378\n",
      "\n",
      "EPOCH100...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9986\n",
      "Train loss:0.00978|Validation loss = 0.01599\n",
      "\n",
      "EPOCH101...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9986\n",
      "Train loss:0.00972|Validation loss = 0.01444\n",
      "\n",
      "EPOCH102...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9987\n",
      "Train loss:0.00964|Validation loss = 0.01482\n",
      "\n",
      "EPOCH103...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9991\n",
      "Train loss:0.00954|Validation loss = 0.01434\n",
      "\n",
      "EPOCH104...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9987\n",
      "Train loss:0.00946|Validation loss = 0.01463\n",
      "\n",
      "EPOCH105...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9986\n",
      "Train loss:0.00941|Validation loss = 0.01442\n",
      "\n",
      "EPOCH106...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.00939|Validation loss = 0.01465\n",
      "\n",
      "EPOCH107...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9987\n",
      "Train loss:0.00927|Validation loss = 0.01462\n",
      "\n",
      "EPOCH108...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9986\n",
      "Train loss:0.00926|Validation loss = 0.01443\n",
      "\n",
      "EPOCH109...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9984\n",
      "Train loss:0.00915|Validation loss = 0.01558\n",
      "\n",
      "EPOCH110...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9984\n",
      "Train loss:0.00909|Validation loss = 0.01414\n",
      "\n",
      "EPOCH111...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9986\n",
      "Train loss:0.00902|Validation loss = 0.01385\n",
      "\n",
      "EPOCH112...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9986\n",
      "Train loss:0.00904|Validation loss = 0.01417\n",
      "\n",
      "EPOCH113...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9990\n",
      "Train loss:0.00894|Validation loss = 0.01341\n",
      "\n",
      "EPOCH114...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.00885|Validation loss = 0.01366\n",
      "\n",
      "EPOCH115...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9990\n",
      "Train loss:0.00880|Validation loss = 0.01400\n",
      "\n",
      "EPOCH116...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9990\n",
      "Train loss:0.00875|Validation loss = 0.01407\n",
      "\n",
      "EPOCH117...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.00868|Validation loss = 0.01438\n",
      "\n",
      "EPOCH118...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.00862|Validation loss = 0.01339\n",
      "\n",
      "EPOCH119...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9983\n",
      "Train loss:0.00864|Validation loss = 0.01385\n",
      "\n",
      "EPOCH120...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.00852|Validation loss = 0.01276\n",
      "\n",
      "EPOCH121...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9990\n",
      "Train loss:0.00849|Validation loss = 0.01232\n",
      "\n",
      "EPOCH122...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9987\n",
      "Train loss:0.00844|Validation loss = 0.01289\n",
      "\n",
      "EPOCH123...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.00838|Validation loss = 0.01351\n",
      "\n",
      "EPOCH124...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.00832|Validation loss = 0.01281\n",
      "\n",
      "EPOCH125...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9990\n",
      "Train loss:0.00828|Validation loss = 0.01372\n",
      "\n",
      "EPOCH126...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9987\n",
      "Train loss:0.00827|Validation loss = 0.01242\n",
      "\n",
      "EPOCH127...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9984\n",
      "Train loss:0.00848|Validation loss = 0.01272\n",
      "\n",
      "EPOCH128...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.00814|Validation loss = 0.01206\n",
      "\n",
      "EPOCH129...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.00808|Validation loss = 0.01255\n",
      "\n",
      "EPOCH130...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.00805|Validation loss = 0.01230\n",
      "\n",
      "EPOCH131...\n",
      "Train accuracy:0.9999|Validation Accuracy=0.9987\n",
      "Train loss:0.00806|Validation loss = 0.01256\n",
      "\n",
      "EPOCH132...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.00796|Validation loss = 0.01284\n",
      "\n",
      "EPOCH133...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9987\n",
      "Train loss:0.00790|Validation loss = 0.01282\n",
      "\n",
      "EPOCH134...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9990\n",
      "Train loss:0.00788|Validation loss = 0.01217\n",
      "\n",
      "EPOCH135...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.00784|Validation loss = 0.01159\n",
      "\n",
      "EPOCH136...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.00779|Validation loss = 0.01219\n",
      "\n",
      "EPOCH137...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.00776|Validation loss = 0.01277\n",
      "\n",
      "EPOCH138...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9990\n",
      "Train loss:0.00772|Validation loss = 0.01187\n",
      "\n",
      "EPOCH139...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9986\n",
      "Train loss:0.00768|Validation loss = 0.01250\n",
      "\n",
      "EPOCH140...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9991\n",
      "Train loss:0.00764|Validation loss = 0.01084\n",
      "\n",
      "EPOCH141...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9990\n",
      "Train loss:0.00761|Validation loss = 0.01101\n",
      "\n",
      "EPOCH142...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9989\n",
      "Train loss:0.00757|Validation loss = 0.01163\n",
      "\n",
      "EPOCH143...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9990\n",
      "Train loss:0.00753|Validation loss = 0.01121\n",
      "\n",
      "EPOCH144...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9993\n",
      "Train loss:0.00748|Validation loss = 0.01195\n",
      "\n",
      "EPOCH145...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9990\n",
      "Train loss:0.00745|Validation loss = 0.01135\n",
      "\n",
      "EPOCH146...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9990\n",
      "Train loss:0.00742|Validation loss = 0.01069\n",
      "\n",
      "EPOCH147...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9991\n",
      "Train loss:0.00738|Validation loss = 0.01060\n",
      "\n",
      "EPOCH148...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9991\n",
      "Train loss:0.00735|Validation loss = 0.01099\n",
      "\n",
      "EPOCH149...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9990\n",
      "Train loss:0.00731|Validation loss = 0.01181\n",
      "\n",
      "EPOCH150...\n",
      "Train accuracy:1.0000|Validation Accuracy=0.9991\n",
      "Train loss:0.00728|Validation loss = 0.01087\n",
      "\n",
      "Model saved\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    num_examples = len(X_train)\n",
    "    print('Training... \\n')\n",
    "    summary_train = []\n",
    "    l_rate = 0.001\n",
    "    keep_rate = 0.5\n",
    "    kp_conv  = 0.6\n",
    "    print('Pre-processing X_train...')\n",
    "    X_train_prep = preprocessed(X_train).reshape(-1,IMG_HEIGHT,IMG_WIDTH,IMG_DEPTH)\n",
    "    X_val_prep = preprocessed(X_validation).reshape(-1,IMG_HEIGHT,IMG_WIDTH,IMG_DEPTH)\n",
    "    print('X_train preprocessed dataset size:{}|data type:{}'.format(X_train_prep.shape,X_train_prep.dtype))\n",
    "    print('End preprocessing X_train...')\n",
    "    \n",
    "    #map for reduction of l_rate at different EPOCHS(first elt)\n",
    "    \n",
    "    for i in range(EPOCHS):\n",
    "        #scheme to decrease learning rate by step\n",
    "        if i >=40:\n",
    "            l_rate = 0.0001\n",
    "        \n",
    "        X_train_prep,y_train = shuffle(X_train_prep,y_train)\n",
    "        \n",
    "        for offset in range(0,num_examples,BATCH_SIZE):\n",
    "            end = offset + BATCH_SIZE\n",
    "            batch_x,batch_y = X_train_prep[offset:end],y_train[offset:end]\n",
    "            \n",
    "            sess.run(training_operation,feed_dict={x:batch_x,y:batch_y,keep_prob:keep_rate,\\\n",
    "                                                  k_p_conv:kp_conv,rate:l_rate})\n",
    "        \n",
    "        train_accuracy,train_loss = evaluate(X_train_prep,y_train)\n",
    "        \n",
    "        validation_accuracy,validation_loss = evaluate(X_val_prep,y_validation)\n",
    "        \n",
    "        print('EPOCH{}...'.format(i+1))\n",
    "        print('Train accuracy:{:.4f}|Validation Accuracy={:.4f}'.format(train_accuracy,validation_accuracy))\n",
    "        print('Train loss:{:.5f}|Validation loss = {:.5f}\\n'.format(train_loss,validation_loss))\n",
    "        summary_train.append([i+1,train_accuracy,validation_accuracy,train_loss,validation_loss])\n",
    "        \n",
    "    summary_train = np.array(summary_train)\n",
    "    np.save('summary_train_'+model_nbr+'.npy',summary_train)\n",
    "    try:\n",
    "        saver\n",
    "    except NameError:\n",
    "        saver = tf.train.Saver()\n",
    "    saver.save(sess,save_path='./traffic_model'+model_nbr)\n",
    "    print('Model saved')\n",
    "    '''\n",
    "    ##Plot loss\n",
    "    fig,ax = plt.subplots(1,3,figsize=(15,3))\n",
    "    plt.subplots_adjust(wspace=.2)\n",
    "    # set font size tick parameters ,x/y labels\n",
    "    for i in range(len(ax)):\n",
    "        ax[i].tick_params(axis='x',labelsize=12)\n",
    "        ax[i].tick_params(axis='y',labelsize=12)\n",
    "        ax[i].xaxis.label.set_fontsize(12)\n",
    "        ax[i].yaxis.label.set_fontsize(12)\n",
    "    marker_size = 8\n",
    "    ax[0].plot(summary_train[:,0],summary_train[:,1],'b-o',markersize=marker_size,label='Train')\n",
    "    ax[0].plot(summary_train[:,0],summary_train[:,2],'r-o',markersize=marker_size,label='Validation')\n",
    "    ax[0].set_xlabel('EPOCH')\n",
    "    ax[0].set_ylabel('ACCURACY')\n",
    "    ax[1].semilogy(summary_train[:,0],summary_train[:,3],'b-o',markersize=marker_size,label='Train')\n",
    "    ax[1].semilogy(summary_train[:,0],summary_train[:,4],'r-o',markersize=marker_size,label='Validation')\n",
    "    \n",
    "    ax[1].set_xlabel('EPOCH')\n",
    "    ax[1].set_ylabel('LOSS')\n",
    "    ax[2].semilogy(summary_train[:,0],summary_train[:,3]/summary_train[:,4],'k-o',markersize=marker_size,label='Train')\n",
    "    \n",
    "    ax[2].set_xlabel('EPOCH')\n",
    "    ax[2].set_ylabel('LOSS RATIO TRAIN/VALID')\n",
    "    \n",
    "    plt.show()\n",
    "    '''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test\n",
    "模型训练完了，我们可以从网上选取一些图片来测试下咱们训练的分类器是否能正确识别交通标志。\n",
    "# Implementation\n",
    "接下来我们加载前面训练好的模型，并预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./traffic_modelora\n",
      "Test Accuracy=0.9825\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    \n",
    "    loader = tf.train.import_meta_graph('traffic_model'+model_nbr+'.meta')\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    loader.restore(sess,tf.train.latest_checkpoint(checkpoint_dir='./'))\n",
    "    X_test_prep = preprocessed(X_test).reshape(-1,IMG_HEIGHT,IMG_WIDTH,1)\n",
    "    test_accuracy,_=evaluate(X_test_prep,y_test)\n",
    "    \n",
    "    #select 20 random images\n",
    "    ls = [random.randint(0,len(y_test)) for i in range(20)]\n",
    "    X_test_select = np.zeros((20,IMG_HEIGHT,IMG_WIDTH,1))\n",
    "    y_test_select = np.zeros((20,1))\n",
    "    \n",
    "    for i in range(len(ls)):\n",
    "        X_test_select[i] = X_test_prep[ls[i]]\n",
    "        y_test_select = y_test[ls[i]]\n",
    "    \n",
    "    test_pred_proba = sess.run(softmax_operation,feed_dict={x:X_test_select,k_p_conv:1,keep_prob:1})\n",
    "    prediction_test = np.argmax(test_pred_proba,1)\n",
    "    print('Test Accuracy={:.4f}'.format(test_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9+vX47W9/i3w+jwsuuGDYfBiVsMWmpqauhx9+eExnZ+egTquDQcuXL8fy5csP+nMGUj6fx6JFiyqe5w0adN/c3PzAZZdd9uH3v//9AAZ35h0Mer348oEPfKBvw4YNZ2itn9nT70qpCxYuXHjPnXfe2QwcPJ6sXLkSS5YsQWdn5wFtdyCtWLECDz/8MFw3Aa1rSW+ycZWK+aGELbYlk8mtv/rVr5LAW2OePPPMM/jsZz/7XG9v76A2rJaWltVf/epXZ86aNWvUeLJlyxY8/vjje9TSDjb97ne/w5VXXvmH3t7et+3tulGR0JVSVVE/lixZMhqPfF2pXC7Dtu3q3q4JgqBYr8a9FfhSqVQsAHvTXSulUslIGG92nhQKBQwDwmYwqgZB4Ej0xJudJ0MhXheVfVxWeavtKdg3T0ZHQs9ms/96xBFHXHT99ddnRi9T9PWhSqWCL33pS6Xnn3/+B/l8flCdSSm1pKWl5Ttf+MIX0hMmTDhgmaJvRAqCAD/5yU/Ce++9d0elUpmhtd7jxFRKdSaTyT9dffXVzSeeeOKbOlN0zZo1uO6666A12DaqatgBvOYK+b4hvfRsNvvsmWeeOfuiiy5Kig34UKW+vj589rOfLe7cufOGcrl8x2DXZTKZmydNmvTpm2++uUns+Ycq9ff347rrritu27bt5nK5/M97u3a0Uv/tbDb7NaXUx6O959tjN4lGNabsUnv8p/TdYHjUJ+fqho96HI/ab4OMXVl1z5APXf/nAIy9GlmWBa31A4VC4W+1ZtCNQchxnKVHHXXUN/v69glbsU8KmK8VkfoNhkgdNbJr4Nf1tyGRIIyLJDuPBkEGGBJZloWenp5fFwqFC7XWr+3tWqXU3Jbm5ueCIGjs1z7Sr/lmaYM+B9xcm+t1DFC7fWMeowe85RrfapNjj73hTTwIAgOpoFFDfZRuFIdgcuHxtGaz2YeaMpn3hVHUMG/1IPO8cSB7TtW3GFdF+hiFAZTguijBJ2Il3pZrFaJAnkl4LBFjxRiUS9s2KJOR4X09QmU9v2urVkMhkXCRzxdvKpfLN+m9bE5KKdXU1HQ7lLpSD9hTTNvmADX38J8aA+8ZeLfgAdlWzVGpo3CPd0ApgK8TnCodBY3tKWXmj0bj5qKjgbtKI1mWjSgK/7FUKt2wN54Ao7ShCx1x+BEE/8mTJYxq8KIRD8ZyaAOxOBLAcVzYDHyTTRPkZYZBbrRfomsYYIfAhfhFRbSX+gza4/tkAQkCD16J7lMV+hTma27HzbYg3USaRBjQfUWf2iswUE+V84Ysy4ILwUihZ6/789pRg8+VKfbrTYQFtvIfb6X+7SIYjFBHZjcOQ1p4CZfGWVtstRjpHb15AMBfnvlxAMDfXfRBAEB2wv70EsAwbA+dY8ZqAPCZn14QIJTXahaiLE7+SwFgAKiWZorfTYEBjooEH6t9OV8tAHbdjRQvThdxi1FgFr0WGF6GFHZTxD8/rKLKbfqCD2RkBQFAqx2sI93Qhe7/xj0aAAKP3mPoV+F7NIcFFEqAswQ6VkV5wOPxM2BUmg/pmdPIQZ51KQZ8+/pnkUoQKsO48eQ8bp5AYXjZmccBAJLt47D5N130jF1PAQA2bnwSANCziyBfm444AtsStGa37iJHYs8OuqfKgHhJfn+urVBi3vl8eNz9r98dMl+a22iuWIbvEcYyPC2mUrKp3SeQtgRXW9ZFdHUTOJcOmE8M251IEBpds0X9nH3Y4SgxwF9lM4HbBSHxWmZisjWHYMosAMBrXdRevpscs0kGO8tlckhy9E2V55PPc6fYQ7wRkLIQqGHD8ITq3rllSDwZ3SgX3jiVwxuwZZnN3eXAfduliSCHoetmkWRwcivs52Z482d0Mpsxz9OZFtgCZA6WHngTM4BcOoBXINCySi9N3ko/IRNWisTgSqEXiOilZXhjzyRpA6jyNYGW59RQd0bzcBQD/dOMpLjyVtJOKz2bAMAgQFrKRsjIdlGZkN96qnRNWzuFUQWWBYsls/EtpL7+4Im76beQ3se1SxcBE5IHbTz11NJOG3Jv91YAgKdVHapho3RjcahXBBstzYTd7rKpMZSNnDfV0KaNLN3UgqYWcoimMzQ+2WwjLXNgF8qMTlhh9L6QBYiIF14inQEcvs+nDUDmgAgqlpHcot2QGYdL27fTu/arDLoX+Ca0TSvBbGeUTH6Wq31o3uRTLLFOaKY5PS5BfOreSiCYE5vHYOKMkwAAY446iu6ZQpmzqIvLbz6bUC3C9acCANRzYwEA5ed+DADYsmUnosk0typVSlIqsBBlBQKMx2GOCrB4A/a94YfpmXNYtAMdmjoJJUZM7CuTBlz2iW++Thi+OQxYl8rQ3Egl6XN8E+0pUCmUu2gjH9vCeO9HkvN163qan2H3K3ALtIc052he5XsYXZI37XKlhEKlyM+gdoI6oRCoIWemUxnDC9/bp9m8kR/DujqmmGKKKaY3LI2qhJ5ilcrXUh3GqanMYmOTYHqbJSfbgyPVX1itVCmSIpVLJphEC6lJiXTG2HrFvKBDrmpkAM1DJPgUtnLUTq5C9oT8NkpJ7u/aAb/EFVZY0mptI+lPRVz9pUynbdnX8AZIYwebQgAv7CSJ4Gs3LgcA+D0kvYnEWqsE5SEqU5/dApllkiyF7txJkkf72OkGGleGMI55+qOf3kn3qgr+4eNkfkm3H4RB1VGoSKNIsNZWCQPYYosWCVg0JE0dTmebkbQ4uaQopjh6z4k0SZeZNpIk3Uwais14IevqobH50nxx21JINI0BACT7WIPjSjiVEklNfkXDSaW5rzzPeI4GpopWzUhv7OkjVOTS7Nco8/PL1bKRTB3B5BdcdklVdx1oi7XMJpIMU1xFp1whibUU0vimzTgFY48nM4x7GId0pvcwpxOsDcykNTGt/T0AgC1ckWfdml+ixSfet7pkZuh32HTJJlCPTZe2YJahZrceDhkNTfiQyKDMFZhclm5bm2mf0GyGDSu2wW6HJZXRaO6MTdM9Y5tICyn39yLpk4Q/9VjCVcmdchYAwG9dDwDY/L87YHexhD6VtUuW1Etdf6b+eWXYojmU89x74pHR5iCaShmKTYIKw3OCxxJ6TDHFFNMhQqMqoQd8QoVs79NKmRPFcUTKpWtS7Lixwn74BbL9WjZJjQFL5i0t5MxJpPikUwEithdL3UNT4II/Qw1otjPayRZ+Nn22TBJprYxiN0l5QYmdoRad+hmuA5j2qU+BH8JTUvHo4IYeit18Vc9OfOVz/0jP7OfKK7ac5GwvZiklrBQR5UnzsFlCkTqR40KS0Pq6X0PLeLJ5SkUmze+jo5V4/ciP7oRrkdTxf/6/BfSoloOjkXj8DsHSV8LVqHjsBzBBHdQ/hyXtpO3AqnKlGr7daqK+J1tI0raSLJUrC7ZqjL6weE5KhR1EgObr02NIWpPaogD7XsoVRAG9e4kGEn+FLxoi36F1XfTNCKfJ2Haa76FH782FRqVE0p4OSPIVKTfiCjx+YCPDayodsdbrkEb68mtUf2XmLFpX7SfPRHIcq19q3+9WtOEEKT6Y8w5yDG7YuhppqXiVondQyZCUu63M/WLjdxCEBvgqnRx+SKb4M6QiklIKim3Uzez8LzbR+LrZBxEWe2GzmpRI0LMTrN2Nb6Xx27yWq307MGEM7Q8TjqU8p0znNABAb0Tfd6/+PSobfkP35WludIyna/7cR/6qhA4hjnjF0UGyRpUjWoaSQUFriSraN1xHPcUSekwxxRTTIUKjKqF7Ui2cRRTbqVVftzgCweXK9EmWRyuFAuyAJRsOhUo0k+fcYfuex2FECAIjcUUiLUvNUiVxsFGdJM0xsRwZo5MkybWMA+ATQFG1jySgapnDnBw6/TMJ6oPnleBLeJseJE51P0liI17oJxv4V69aBnA9wsjEzXIfJAST7YdR70Y4HMImYYoeRwlx4A5yQRcKOzm8atw0AEAtCpx429k+Fg/94Fr6TV0JAPjUJ/4CAJA4wHkdEpYn+Qc6iupSDmS8bDMWCcu2EHBFeZvfUYIrr5soigq9yzDw4XO0hcRVJLIkbWWZJ+X8LvQUiIeJJmqnKU3SkpsjG3To70AgYW9cuV4AllzTvtj8ayXgTIjkMMlW9C6a0yy9lSNoiQEXftgklUqopYp85Ph5M1pI+oxC6uOxbyeUwYlHUd2EVG4GoEae+Kd5ImSS45DgCNG2Vq7VmabPPvZZJFwaQxXarJvIH758KUjxAWtGth2i4tFa6KnwHlIiqTnkaCUVVUxUUxPvN80JkvCzDmkUBQ7BTEQldB5B6IvZGZR1L5rbuIlkL++adQI2bHuFni+29BbyL7SMnULj7H4NNu9xHHRkfIPaGhD7D8DKklZRzg+PJ7GEHlNMMcV0iNCoSujGbqnq/jYx3Pxp4sZZdgoCaK5hnspyVAsH6AdsEzbZbTqqJYMY6Q58TZ0HfUDQvkBv2hbbGNOtSLPtOOTY7aDKcbMeXeMm6XfH8aE4aclE0hwgkjSY50uUeHDblZQ0ZPd3AeIHkBhWToCSauWqj2BinbBieCB+BJHUSxKf7AK5kGKuS7tIwkuPITurJAU6toPOdjKWPvIY9aPJpms+8XfvoItSB0Y+CFm6Rd27lL77/K4024VtlvQQVCAKkpWkMTgpjjwJSDIrlPhdhhGsSGzd/EzJKgzJFt1T7EVTgiXxEkUJldku3cQxym6pgKDAsenMS8eW99Loy1FWnQQ2Qhu6X2S0P37HCVshlaG1UeEXFUYOf0qCUQDFfUoxX6qMGjgh08zjofdoOVmMRMYTvTR7LNnQrd+uRrSNtMkwJD54fFWVxy6Jg0nXMVJ2KlErrDFUsgYw03YdJHiBV1l7qnIMPLgv9SWYXV5lYzkvQTP//CJJ6BM7WjFhDs1vm6VusTDkeO5NmnkMul8+FgCQX/dzAECilxL7Otrp+/V925CwJPuWJfWQ3qPFOTLiP4y0BRV4PL7hRf6M6oZusaPNlrRYbQMcfhawQ8BK8MKt8kuIIoDVoWSGVOla8pAozHVqrejmJkyRm6kLH8OA9GPZ/CMl2ao2HA7xsliV1qzOR56EntFCchJluJICHR6YDV1e4Z882mS/fAWZOKwemmSWnQDEhARRV7l/HDrW7HCIlmuh5A1MPqmlowOAH4TIuDQG16dkid5uaj/byqVRVYSIzTrjxtF3//Y9gsUNrOUAgL//h0XU/n7OKtmAJMvSUhYsNKZSS9q6JAVq36+FA7JqK84mk9DDC6mtdSKiKqnhJVbLfW4/qNJny9jpaOZwxzI7tsrsmPW4nXQqBVWgw0LzQaNsDv8cMCYSWNgcGA1vkQpt3s6VfjjpDWEIn3kkZr8qbwQVDg90EQBZ2ShoA+kqklN1HifIJCeQeo/E8F6cjEJmfcTmKifZDKCr4UfN8bAer3eH319bc6tJvtH+8M09tt2IjxNFkdlwQ573kkUq8wFaI8MO0yYWQjJpElZK3WTKTCji1aQj5yI9+RgAQOCQSWkLh426O+jQaknZGHckFczIb/kjPaKb1m5rK82P9o4pKHVv5j7KfsWHcCAbOnUvmxuL/jKv3+rQIHuFYpNLTDHFFNMhQqMqoRtJWCSUCJBInUCOlgynzPLpmtA2LE6oSHCatjiVahaO+uQNfhY/I+DTT7P0Tb83VhapgfbwvbYFK8mVa5pISisWSOIQSawqEpnrIMFqWnSAnKLrInLe3faZGwAAdj9JWw5jiVgqMs7PgCvF+H2UwADG9iizIzmdUCY0S5InZJwOS00hgArzK6FIMkiWSfro5yE1jZ1g4tQSrB1MGUuq+re/dzMAIJsip9HSS/cP+EU0MKVJEna0Mlg8NWApdo7qWsq3cXYzHlDEoZzJJCcUNVG7NoB+Tj7yQg5DzfLcKrMjLdTQDie/8CqxqoK1wVK445pELGPi4HAzSTIzkrtC3eQcmYReDGgdhKxFIPAQhKzJinjHnXWTNFeSVgiXQyo9Nif1KsYwmsLvaZiSudBgliPLQs1bKUEKbEL1OYChxCGmlaACly9NO3tFnN4jZdIc3szj97SGzUlLDn8XscYm78gG0MTWgvYWmhscQYhyP2moh42ndd86c64JlgiKdP/qNbTWXv3ZvwMA5r1tMjqPOgUAsH04CuuUAAAgAElEQVQtSep9a/6X+tVLwRUtudno6aHAClWl9W2SonjOhKxdFQo9NcyiYe4psYQeU0wxxXSI0KhK6Fm2mwWcCFQJAc2p25KmndaScCCQmpZxconB1ADyiJNJUAOtmnNVzjVxUlk22z+9CsCp0Mqq2dToo+aEc2yWhvl0r2EUcOglS8hhFBjUtAPBzgDAvXdTWS5nF2kFAi3gGOevQsDhZR4nDVlsCw55/BKaGFk20owk57hc69BnFEMZdwRYhgesrUh4YJX7kM/ATqe4H6yRsBNn+nhCtXvhj1+gQYRfMGCGI6FUkpyO2uYU93zP7s5usT8aSFJtBGATCsvzTRA8RZq3vCI85leSpS+T0iJO5kibJCZJNtIixhmbvF3DsRfgOdM/QWoUOVZBG6fcyOSoIof1aY/aTFlpuAxQFzmitYpmS3NyTDaNMa3kbBQnnE5xn9o43tTej5dVRwKoV/UrsFCbWwBgED0gCTMsNVd945uJwgDDJV+82jzHI8uBJbAL0iFR3Fjzs8MIrU00l3NpCj0sdpH07DqkxeSmUfLculI//vsrn6NncRLfpMPIh3TaSacCAKbMmAs/Q+2Mn0UO9L4tBHjmcbu55gKaxxCCZXHbq9QPfkdmXspr0AHYlYhwmO8mltBjiimmmA4RGl0Jnaut9BuzUBVavN58YnsFsi85dUUJQvm3CeFp9K9b7OmONMwxJ3ZYy9xC/3DdtIn2EPu9SVKpk9TFHGvCogaaPyXkMdIIWRIIRhi9UE8OgCVnnAYA+MqrvwUApHrYXs7XKGVDBYzpniM7qMdASwmBFmaJLQxDEyrWlOSIIklG4jC8UNck3kDAnSSEM0U2xmQ2a6T/iMVvl7WYTDOlpJ96+jJ60H4KfLlWhngQMCxoE/kC8YnUYbkTKSjWyyy2O9ryXiWtX3DrvRJ8Tq5xGCPbSfP48yRhETa1jJefYHgamD6YoCq+RnD9IxNeVYOgEC0jGqENvZn76vOcbGvp4FBDIIhIsiwXt/IYGZs8nUS2idZdpY8jcizp094LKwyVRCqU4LX+cj8kEC305Du2cfs0r2wJVbQdA1sAbyQ+qEboWeioBkcrc8QAcdHfactGG8NBVMsMwtdDPqNJ0yg00ZlwBADg1a5+dE45HgCw/oVfAwAmcAjzpA6ap01jOlBkULeJR5G2uu1lggnIv/wjAIDqW4+OdrKve720bgIBeeMkQPG3WKiFI2OYYYuxhB5TTDHFdIjQKMehM/AWB+TrIEIQSPws2/7SZC8PuFawE4WwJJlEIFM5esFhiV+ZKkWhsaWZ7Oo6CQ4AEIZ7KNIl8AOOuUYkq3CAl1lspBLLbaMGMGbtp6QjtOAoinvtPpOqi9/7Pw8AAJp2kvTo+1UDr5lIUpKV00FJHWH3GmqEizEoKFS5W7YnyS+Np3+I2rsJuWqTSpME09RKEnqEmr04zenRLWwTfO9ZHwUAnH368ICEBqPeXZQ+rSucCKSUiQiwB0pd8l5tF6LDhL5U7+F0cI6SkoiBildBaDHfuPpQwua4X7tmi1Yi2PGTpEiKgZfwfBONoJQU/xgYFy12XMtoj9ag8SF7J4sB4VKcl+EHfUi5XDhCoNsiksIdlsKTCQsJTm8PRNrbLV1i/0DlhD/bNpBtuLdvLVLsE+OgIewqkMZQLlNcdcpioDjXgiva8IhqyEpOifguav4z0c4lIkrWZ1smhaYUA2ttJI2myaW7ZhxDELnFsdOo3fJWbNr4OwBA4NP6yyTIhm6xxhQoBwmeN+3jyP9z2Cyq9PTSa88BIFt6axvd38trahcnrBntTmDCoYyPbrhJaKO6oYuBXzZOW7sIA8EZ4Q2UmW/bPOvC0Pxms4qfYJTEhCvoZYxB7VfgSykuwYcYoJprKLMxycIaYJWBpRQi3hT8yoAi9UoSI3gD1A4sxs9w7AOzoQudffY7qT9JGv+PnqKKMDvXrIPijFrBDlFpzpwcT5Vm/O4/0PfVCDbzosQ2JlfCt4QProOQTV5IkfnEzrJjEmLCSiLLKHbjx9OB8+5TqEzdme/lDL8DVL+4XKLkDsWetACRSdhxOHvFYM2IA8xOABz2FnJonM+qsc0boMVVciqVMhx2hopQIBWbbA7xc4oV9O+kTcgPqD++5lKIIhtUS8aJbJKYJNFLwimlXidUrTbuSPdPTmBLiJmhnEepxGPld+xV6W/NFZS6owI6FI3VrnAVI595x45hpOTkGpnCLsPpXksberW4FciRk7CfHfAVFjAkPlDzWILAMiXohu8SrYUci4lLq9oBowaYvXIc4NDe0oFqgecWoyNOnkmZouNmU7KVP5FK7m22krDW0qY8kTGkEjnGknKl2pVlMqqTCXp65zFkenntRTogKqufhOqhAIb2MUcCAPp62FktVYlYGFU6MrVi91mCeQDFJpeYYooppkOERlVCLwQsXbGXLghDg2csDs+Qg+xTXJXIcRJIcuWYFJsBcs0sXbHahsjldm2UTQo0K14i+Tc4LEVCBz9zwDEYhPC5/l9UZtMF3y/SsssqesXzEUrTI0TR2xeddfpCAIDmxJBHgiqC9ZwGzmqay2GMIafwJ9spycfrWQ3wGGzWtQPhCYcfBioJK0OSucu8thxJkCHtozmTw5QZJG3Mfed5AID3LGDJPHHgxgoAyYRAPHCsW+TVKllxKKK8sVA0sqYcEoxNYiq1s0dOTDABS60RNJr4WteEwjL2eTMhElb8rejrp5R/2GSeaeZKPwmf5oZXKRmdWLDSPX6mqVhk0CE1zLxTI1t2Nec/wzQkXERVqcLDWD7iYGPNt5QPUMrQNVPZEdjNzmbVQ5oHuLrOsMU7UaJ30lh3vbQaAJDRVYQuPb/Akrng7LucxFTlxEFJ0ANq2tKwSGA89qD8mHrFbHtp4frAiUQOfTvo3ebSNIhpx1KlJmscOUN3sJ1y7Z9XY6J0q433oQ5aKwFj6rhBnXbPWmCWl8b04yjh6KVNz6O8g8w7ra1Up9Xh9+H1c41YCDZVzQw8XId1LKHHFFNMMR0iNMoSOld3sckGnnIsFLlati32Z7aR2hmuHIIcsu2EKZzO0skoklIUkI0z8sRhBmhOnw09+jQp+iYbCZBzXE51c6qJbTnwUCzQqVllkLBQsa2VnSehJgeV55cRmDTd4fFjuLT4zDOoD+kEHvnB96h/m1nasqSKOVeEYbux0zodXvQyXVPlME9xgDKKpZWZiESWNCLBDpdQv5Zm4vVRR8/H3PnnAwDefTxJsxA/4AGmCtdcFKdkYCmDoCdOxsiklvN4lYMmrkyTtBt9IyK1WuyoyzRPMM53u86ZBgDaJe2gZfwU5Mayn4JVkKBEvC5upznrBxHA/hPFFW/8KleK4mfXHH3K9GjkuPkCO0HPyJdKsCSRSOoJsJ/DU6LRVFFlaTPfT98lPa5t+TzZvN0x0+nSlsSw7PvyLrrWUyq87qb12J7OYU0/Ofx29TKKZ5m1AQ75lPenoWELX0aQWGTkWJP4p4zzU9Z3hiX/ZsYYrxZKCIr0LjuOJGm5/ShKJCrYtA7Wr6MxtQb98NL0XdZlcC6uxpTtIo0vGW7Cy7tov9iw6ocAgLdNoP3ryFlLAACbps1F8SV6ZthFAHrjWBv4czdpi+DwTbImyBwenhE9ltBjiimmmA4RGlUJPcXlYPw82ZKisGo8/65D0qKdJONTkkGxUqks0i0MlCWVWtg2lk7RiRtaZNOshl4tEcgAodOHqeyjNWAiEQYAPIUkGXr921Fk+6IkoICr4LiMPx1w2m4QeghMdaSDU2NzIL3vlHcjZCz4//gxSQSVzeStF9x3AQrTykaiTRIaqKqKqVfYRJEIiUwzIkm+YRt1M0cpzJhJdvNjFizBScexZD7yojZDIpffR4W1qzCyUNKSNs92aAOVK9ElFiKGRPXZhp7k0L2A6276HApmp5tNEpkKJTxMIA/Af9tICKY5A7OVdhD8aVDmyCcrAZsjHSqc6GX6jHqNEICOajC+IyQl4YeB5LK7cHn8AsegI3ZoMA/ylSq6WPCd1HEYXVOg2pqb15LmljnhaABAMjsFcKT81OBz2Wd7/WsvkM38d08+DgBIsLbQkp2IaDvxyue4xYB9HSI9JxmW2nFsKNZi/BEk5g2MHFKqlovDSBdoYYO2rWgNF7q2INdE45t63EnUjw6qqVuo0PsrcwWiY1oTCGa+BwCw48VnAQBzjyb/1DaLtNdn/vgM/vQ7+q2y+ff0rDE0L9q42tWMo4/DHzZR5Jm3i+z37e30PraPobVWYKsAohLMxBlm3GIsoccUU0wxHSI0qhL6cUdxfb2dZEvavnkLdhQZNItBdmyTBMFAUOkslCsFDxhClT34rhRsiCTW1UfA0RASeVKLLedOKEBLIQqWYl22SVZ6yTbas+01VBlgXooiZHNk57LYVtrLVVACVasQLprDaNDZp5I9vZ958N8/+C8AQGkrSV8iwUZKIRBwrlaSQiSeWdsksYSRMjUeU5xwcdgkSlQ66ZQLAQALZ6UPumQuVDVAXBz5FPomjduk8UsMvfhBogBVzhmwUxSVEvF8iars7+DfdT4PL0HXpDjywTE5DVJ0wEcf23+DMklOIc9RAWyz3CS4O/DKPO+MYlgnMoI0J4l4GWHmP7xCP4+LJc1yBVUGMHMNTO0AcDoFKH5xEfun2rki/frtq+ja//k6AODwM85H08S3U3+txupBAked7wrw6lPElz89S3kRlcpLAIBx4yg/YXspib48V/5hXOyQYSYiwQdgab5aDWqAa2r4GkyERv+VhmW0rBzbzlvZD+TnOQKougvjZxIPOg6nFH0vRfvNto0EAZBkCIXOWbOxJUvwGm6G5kyZ958tPml+L/7uOeRfewEAYPEc2biD9oc/PvMDAMDx8z+K9qlUvajrRdL4Et0UqTZxAsWlr9lBaw+FqgEWe0MnFo3J0OMKaXIY9NhdiEIyl/iyECTsixdjoLUZVeARAyv97PjkJAXBfylXywh5wlj8Vl2TuSdhjNqUnAsY/6TYT+ph7zZidLXiG5U1kWbHITsAy7wpVAJxjtrGkWhZ+6dSj4T+6t2kDo7jcMVv/gdt7GoLTchQ2VC2JClIpqIkREhxawtJTtbqnDQbAHDKGRcAAE6czbv4gUkCHRJVfXGO1ZLCJFNUGTREQeiTsL1CDT1SksLE0e7XEs8AyvD0OHOxtGuAyi6d0FENhVPw89mkY7HjFJaF/hK14wWNK6/m+KyhV1rmMBoiIwZQJA5/E+rnw2MnrISZOuLw5izZKApQCWjcm7tI1Z9+GCW9WAUSUl75A83/Us/jGDuL5k2ig7KAI/Z853fSWtm2ei12bqGNyA7pO5eT0LYrWhsb89tQrBJftBRwN+UP+cBh82QqmYXmjDRtKpANgycDcqJsrZDgQ7aNi2IrnrzlXVzNqzmDybOorJzTRgWy+xi2s1igazoYibJlwgx0e8SDIttw1rI56bmXySyT3/onOLLPMLJnnufO+h10CKfX/Aod0yg0sncjITH6u9YDANrHHMbPovDR3j/noTQnYsVhizHFFFNMb00aVQk9z8H8m7aQCtxVyMMzYUx0TejRKV8t08lWKqVqdSNZQtdFOrX8flaJi3yP50GQWrRUtLEakzHCKERV6kAW6H6PizAHjDOtnRTcjFQsYtxxLUVuuV2GL1DahkhhieRBiuMbAr33nVTPM8/Vkx568GHq065eY64QP5fHZimXTS6ZTAsmTCJ1+Zy/+BsAwIKjWP0bRclcqJZSURObDR66McfIRfJ9ET5Xa+LpYbDwmzhJLZkmidRXeWhJtxYbiQFFrIWLOUl2uLL5Icm4Lx47QPOlPnii8Rs0zsaQ2BqyojIhaNFw87mZiuzMDrjoNSINMGRDINjvbqLhs1qtYos4+lxaU24/mRU6WikMOKTliP4/96L42pPUNENbVDkZsMphwF5YQJSg/rd2UrhjNIFC/37Jqf/buzbBK7I2ITnxBlyePiTBqLmtBRkOgCgxds9wSMxy2gQ9RMhKeGqK3ne5l/BjFCNQjp9xBMYfRppomjWaVjajzJtB3zdrCim0VQJHpIgHh73jVOJFgQImjuokTUctPA0uvwebtV6pWJXifchJZNBTovUWTd8AAFj/LL0HbFsPABg3ncxdPVuaodmcM7AI9r4oltBjiimmmA4RGlUJPeghp2NvH0nEXaUIvpQKZy9eyA6siB15YVCFX2JkvFY6cSsM3uQXORxNcIWrHqpcPajiN9obo7qKN3KKOZKkwljGYjdPZ1uQYrtgIsWgV5zSa4CeiyxN+BqhFofPcDly4OnchWRTt0Pi8QPfeRJ2H9k6pbqLVPJJJciWPm36sVi4+K8BAG8TybzRJzaqJJXcRbith22opXhbDX9HUVhDNpT7WKvqLwqQGVd4TyWRSDLuNbfrqEbJOkRYF5pKL7avyMlmAhoWwVS9Mv0yGoM4cWsSltTSHalTNOJKW4GiuRcqy1TWstkBmM6yk7eJpMFqAFhsZ9dVktB39NM6nDzrVADAYa2E99372qvozZMNuczojYp9MzmHeNnWlEM0hWy9fa0kof/+BbKp/3kthyMHRURGMncaxqz4nYQMn9C3a5NJLnTd4WNICAKrsNS1FNpb2vhZ9Fu1hyRzCVHdvGkztj1yHwDAVwPDM6XfHDgQ1d5lTRts1LS0jlCDY68BbAGAzZ++1gg4OdHmMFqLQ4+9Ptpbmsq0ZsdOHIdd6yVpcgA44D4oltBjiimmmA4RGlUJPSrQqV/16aTKtDebxAyXIwhctmkl+NOxXSM9RQwN6rFELkZhsWNGQWSSCsR+6oqtW+yXUEYiF/u6qUWZFDukA59Tp6tlgWTlpBS2iWUYh1zpEspG8jrIuf/DoCXvpiiVXcjhxw99HwBg95AEkMkQrzsOIzvhvAVn4fRjKCIAWbzuFBpzq0QPqVrEiUnOEbz6AfGpqKXb12zVUlGK/u73A1gM7GYZCbsRajXSoYlUqb1Xni/yIK3r8NBV47Uyt2rG/pqUOsIwF7HhK4fCf1OZDJIS0ZHh8Npm+iywRqnK/UinWTJdwz6nKqlfT/3xGQDAcUdSOF37UZ1oxjRqezyF6tkc5eJtplT4pLsB+fE05h/84lcAgO2b6VkOh/SGyoZSktjGWpMluOW8xoxqBfiyrquyrodOAu0v7TU5DtKc/NfNPrZKlTQri2sv+Lt6YfWQlIwBiYgGIUTw7OvgtmXulXkGpEwvVA36l1+44PbLm7a0Mn8EaNTeBMgtyf11nBZYSiJ/Ygk9pphiiuktSaMqoVc4fV5l6TM7ZgySDCbkin3JxJHTaZjQgMsQoSX2tBfY7p7hmM9klk+zFgs+27DEPm4PrNiuLESa44nFfianvCUVfUKEHAURsv2xzBAAmpOHQkj8rw13QNLLG4n+5t1nYeMaij549fd/AgC0J0kaL3Ca95pVvwTOnPn6dHAPVA+0BDCslakA1FixSKT4yLJNsvqg1ePrpGXJV5DCGSKha054CcPQwEXIHDJViMSWanpX17ixFTdGdWit6yTzkUnoE8eS7TpkW3olyEMHZBfPs0+nq5v6XyzR31U/b7RTRqxFidPwKz5JruUXKCV9THMHmjnyJReITZ5B8tro+2RSoVnRM/9yGq3dMYsI3vm/fvULAMDLL76AMvvJLIvj0CVJLBLJl/luObUIIfGBDIMG1me1bGW06NZpFGNeaidtI2QGJPJFNJn3LtFw7Ecz+WBs+7ftWiUy8fNNpIpFehsl8bmWQpAkeV0AxtyBs0MpU/xEtBapG6o5h2An+7h6Nm5C1aP3N9wqaLGEHlNMMcV0iNCoSuidU8lmW5Q48KYWZFMMOs+nVVjhWHP2gtu+B8URKzpBtj+vhe7J8kms5XfXNQUGTDadkfZqYF3aREhwKTxO5ZaydyqqIqpSJE01JGmkyqezL/ADUl8SNVv8cGNGR4M0gEmtZCN97LkHAQBuliSMqPspAMAJF17yuvRtMJKapyZCSauGEoJAnR2aPx3LMtEINQl6wLV1MeES8SI2dEtAzQS+QYWwBVpC0hBNlAP9GWrUBPO6tus/6r+v/XtkvpaQfT99DAHQX9oG16Z5mktTdJLN0A1Bma4tVzWSnDE7ZQJJ2UccSanmbz+BJGulSFMLoxC1gjBsDzdRYiR5OlYLxjDY1RHTKFJo7S6SfGcdQ3zq2lJCV5nzNqokqfsha7zMA/GLWVYSQSilKUcOnyss7fECvMZRLU0MF+1muGQcx5Mj2Qw/xbkFXFYOvAcYnwerDdqy4HMOi/Am4r6HnZN4bEFNU+A5U+RMcl0HS7JlG0UQlXpIso/EZ8DPkkznKPShBCJhmHvK6GK5vI2SV6zNlDqr3VakWf1bt/Z5AIAXCf4GJW84WkOF7JhxKWwxZEdqKIgVnNJdLefhcdq4xxNRME3CgNO/oY165ThseuGQrBTX5Uy6Ts3bYpwl7OzgQyQMZeJHBllPKsqMBsm86xNVVswD/L0pXF3x8IWVNwMAStvJ6RJuEnWYJvFrhU3Yzte38ufrlyIFBCZtnf6udyLWrBYyQnGWBsahZQseum5Uq+Wd6Tr4B9tpDFeUgsIqVGYT07KxyxONiU3tAdt8gHPUwAfUfhvpud/cxnjvORJocn4OFcGn4U1Bwn2tJJseKw7S/N3UcYTqd+QMTgjisDknSZ+ZVBoJrqmqzAbOYXi81ryKZ1Lrq4owmXId9EXfBkoc9FCBtsRZzP2BHLJisiQq++VaUpc3gqqiA6rBhxGwq48OvL5iseFZYsbNNqUxdhxtxkmnhb+jIIc0v7YKz0EvnTJ9F0e3JG0l6urFDnSCizDg8YrcvKML2EX8kr0pZCgHmOLQdeG5GCiEDI1ik0tMMcUU0yFCoyqhawbHKTOgUVvnJEzJkrS98SVKg93BabqupDIrG2Dc4Qqf+r6EllkS4E9SiueX4UfibBEJmyVrrsZtK8ukS4NNLhIqaZykiEziih4gfafYERs5kobt14EljZ5TVJ74kUspIUj/mhDvcpzQUWJppCcMUOgl3gYQLPdG88A9X7sb//HgfwIAWsaQpHLP3d8AAMyeO+PgDWJQIl47Ti1crCaoiEmkEeHScRxokahNdSPWothRVWtD1SWkCHhWQ7PmOgDQgkshGBQizStlHLA1OaoRx1o1mFf2L6y1zM9KOhIUkEU1TVJ7d4W01J7t9K7LbJbJ2EALI2mOZfNmG9cc6BhHJhirldqAH8AvN4YOOvwsiFSabTYVr1SFMeBzNGf6mslJuqnqIeBgghZeYxPbuBJUGzlZqywRF6MQTpXW77a1rw2XJcAAVEJL19U+MHVd+VqpMaqBtibSQTsnEDCWk5vEl1B7TTxZAtNaLZzWODNNNSENi7dSZYlGJsBw/Oh0gNBmy4IgT3LHrAHO8vrw5+Eqc7GEHlNMMcV0iJB6IyXDxBRTTDHFNHKKJfSYYooppkOE4g09pphiiukQoXhDjymmmGI6RCje0GOKKaaYDhGKN/SYYooppkOE4g09pphiiukQoXhDjymmmGI6RCje0GOKKaaYDhGKN/SYYooppkOE4g09pphiiukQoXhDjymmmGI6RCje0GOKKaaYDhEaVfjcTCqjgUYgdwwK5F5fGVs1XDKwyIGyuWCFm0QyTRCViQR92koq03CrkQa4KEHAFUNKDBkaSj3JwDdVb0zF76ixUrc8W2sFpQTGl6uTVEpDRr0cP2G8BgDNFU5CXQ/7KhVypDCDcEbX4WoK/wbwxhR+0HXFG7guqoEEZohiqFqVHy0Vahqbd2zb1KZEVCsUUd+e/B2G2sCHylstlwpD5smEjokaAHyGuEXkI2QY1rYUNbOrSMUBUlyNx7PSsLn2uu/Te0xKNSwuZNLPVdWTmRxcBiAulakIgnIIzhUBV1l3HIDryvo8iGSGiiFINS0viJDrJHjhZO9WAEAfw9j6hoEMq4oQTpKeYTPka9eubcNCRz333NM1AATStK1NJS6H36VdV4cVAFzb3q26k3RNquzUFwaxLflOCmbQb/KOK5UKqlXic5mhmm2X5lWa51fkB6YyUTLB42foX4/nucczwwGQkHqu3JHvPfbLIfPllvt+Q+snkgI0Vq2klFQYkoIkVm3eSwGJeghcoG6PqYNpjhg31xQ/MffwOkWISIqg8DxVDI2rpMiKjsz1wnCp0VKDcLbMvZbZ/+iaK//6hCHxJJbQY4opppgOERo1CV0pdYTLRSEa4Nv3Bd87lHpdXC4q8D34HklPUl5OKrZbqna6hgFJcB5LcnK6S4GESEe1epT8COmlrWyqMWlqItbOxNrJPTRSSrXlcllEkTZg/IGuA8c3kga4X+a+ulZqkrj0Aqir0FZPurEYg23VeCP31yT1RpE/VFbtVQy4xrZs2LZtijlYljLV3dUwIfqVUnYmkyUtRYqcIDT/LnHJMK9KFesDI3WW6yQwGle5TH3ItVJxhpBLFZarDsKQ3r3vU2nCsOrxvaIVKLgsSYtEHHkkvaf5OYFXhWbprcSl4Mpc2EHmQsQlFZVlw3L5u6AiY70IwP+vtd4yBL6ceMLbj4GOtCm5ECozU2oFXUT8riuBZw2QsvWAd5JIuPyZgGMupnY8lqx91haDIDBjNsR/WnWatDZ1XaUurHS08dowDMy8UZYFy1JQSh2ntX5+CDyZ+oFLPgffqzZsI2Ye8t8h80R4pFSt7Mhg208Dh6SwhdS6jaS8jGjxuu4OearwUXhex/U6LcqyHRw2/WgcNu2omjVAK2jRWvTw9pRR2dCVUu9KpVJPnHTSSWjiSiWjR3vaUEaGAV8ul/HUU08hDEKzGY64V0qNS6fTvzniiCMxceLEAZv0m4eCIMCqVatQLpVMJZeREm3mmYfGjGnDnDlzGg7LumsANFZ12Xe71M4sPWu/+rfHts1GShVv9t6rRvGgt7f37lWrVlWVUgu01msGu8txnL/NZrNfGbNPaggAACAASURBVDd+KlyuHHQwaG9TUO/2jwNFjQ1Wq1WsXbPtKcuyPhJF0fcGu0sp9bZUKvXzvg3PIJvN7vUJe1qp+7PaRjLLB3texfPw7cf+FYv/+kq8bcGZ+9ErolHZ0HO53NevvfbapkWLFo3G4w4qPfXUU/jsZ6+FvFal9LA2FyHbti87/fTTJ99www1v2s1cqKenB0uWLAGgYVkW2fyNvX9Y9M7m5uazHnroIaRSqQPezzcgZb/+9a9nHnzwwVsBXLCnC5RSlm3bX7vvvvsS06ZNG93evU70wgsvZP7+7//+GwAG3dCbm5u//IlPfKL5ggv2yLY3Fb300kv4h0/9Hxx74ukAaM3InjJcGWlUbOhhGI6bPXv2aDzqoNPs2bPh+x6Y7QBoQ6///1AonU5PnzNnjvNm38wBoK2tDR0dHVyn04ayLCj5n7KMhDwEmnj44YdHb5HNHAAwe/Zsy7btKXu5pMVxHLxVNnMAmDVrFiqVSus+Ljts9uzZb/7FA+CYY45Bob8XQeCTqVGH0EpDK40oikyQxFBotGzoal8bF0Vj7Pmaxx9/HK2trTj55JMbvr/11luRyWRw+eWXY+XKlVi0aBEeeugh7Ny5E/l8HjNmzMDFF1+MSZMm7fMZg9HNN9+M1tZWfOpTnwKAPZoBRkJKqSH15c3ClwN1MB1onkRRhNtuuw3VahWpVArXXXcd1qxZg/vuuw8AsHTpUsycORPLly/H2WefjSeffBKrVq3C3Llz0dHRgU984hOmneG8+2effRY/+clPsGPHDrz//e/HKaecgm9961vYtm0boijCZz/7WSilYFnWUMa8Tylhf3nys5/9DE899RS2b9+OSy65BHPmzBkVntx5553o6+tDtVrF5z//eTiOM+Q2D/Se8pd/+ZeYP38+jj76aJx33nkHfO0EQYDzzz8f8+bNw6xZs3DuuefWxnGA1s+ohi0CxMhVq1Zh+vTpsG0bjz/+OBYvXozTTjsN3//+9+H7PsIwxBVXXIG7774bWmts2LAB5557Lp555hls374d55xzDn76059i1qxZWL9+vWk7mUziuuuuw6pVq7B27Vp86EMfwsaNG3HJJZdg4cKF6OzsRH9/P84//3xce+21uO222/DjH/8YL774IgqFAj784Q+ju7sbPT09OOuss/DEE09gzpw52Lhx427j0OIYUTX72P5saocCX2heUpipuBjCaORG1wPFk+uvvx4AsGzZMkRRhIcffhjXXHMNlFJYsWKF+f3EE0/EiSeeiOXLl+OKK65AJpPB3XffjXK5jCOPPBJPP/00Pve5zyEMQ3zxi1/Etddei7vuugtBECCKIlxxxRW47bbbcNVVV+GEE07ACSecgJ6eHnzjG9/A/PnzsXbtWnz+85/Hgw8+iD/84Q9429ve9obhyamnnopTTz0VL7/8Mp5++mnMmTPnoPPklFNOweWXXw4A+MIXvoD+/n60t7e/bjzJZDKoVqsYP368aftArp0zzjhjj88wpCNAKza50FdqmObc1yVscf78+Vi6dCleeukljB07FkuXLsWmTZuwdetWZLNZlMtl7NixA93d3bjssssaJtc555yD7u5uvPLKK5g3b96Qnnf44Ydj6dKlaGlp2e23Rx55BNlsFu3t7XjxxRexYMECnHXWWejq6sK6detwwgknHNCx741ivuxO+8sTAFi3bh1uvPFGZLNZWJaFQqGAXC6HbDaLUqm0zz6ceeaZ7CNoJJFos9ksisUiurq6cP311yORIMflY489hmuvvRbvec970NPTYzariRMnYseOHW8ongDA/fffjzvuuAPvfOc7R4UnALBjxw7ccsst6OnpQXNz8+vKkwceeAA33HADHn744X0+byRrx3EcPPDAA1i2bBkefPDBEY91bzTqEjoAhCGHgwWBiXrRWuO4447DhRdeCAAoFApw3Vo4VT0999xz6OnpwT333IPVq1djw4YNe32eeMETiYR5doUTQDKZDC699NLd7lm1ahV27dqFe++9F2vXrsXGjRsxefJk83stV0MNy8a1N3qz88V4FXRdON1+apL7yxMAmDFjBm666Sb80z/9k1nghULBjHNfJHxyXRdhGKJcphDFKIowd+5cfPCDH9zjfeeeey7OPvtsXHvttWbTAoBt27Zhf3xKB4MnEydOxEc+8hEsXrwYX/nKV3DjjTfutQ8Hgidvf/vbMW7cOFx//fW499578cc//hHHH3/8CDhyYHgiB1sikdjnmh7J2gFov3AcB67r7m6u4QWkofYZzT0YvS4b+m9+8xusXr0as2fPxksvvQQAWLBgAW677TasWLEC+XweV111FcaOHYv7778fzz//PCZPnoynn34a27dvx5IlS3DGGWdgy5YteOSRRzB16tQhPffoo4/Go48+iiAIsG3bNgDAokWLcMsttyCZTOLkk09GFEXo6enB4sWL8b73vQ8bN27EY4891rCZHyyK+bI77S9PFixYgG9+85vQWsO2bYwfPx4XXnghbr/9dgDAxRdfPOS+vOc978Hdd99tJO2FCxfi1ltvxYoVK9Df329U7muuuQY///nP8dxzz6FcLuPss89GMpnE4Ycfji9+8Yvwfd9sMm8Unjz22GN49dVXUSgU8P73v39UeFKpVLBixQoopVCpVPDhD3/4dePJnDlz8K1vfQsAcMIJJwzZjj+ctXP00Ufj29/+NgBg3rx5ByW6TY0k5G641NTU1PXwww+P6ezsHNSRt7+0cuVKLFmyBJ2dnQe03YGUz+exaNEiOA6d8ErVUmckRb9YLu7zTTU3Nz9w2WWXfVgWz5udL+eddx66dnbBtu2GaJ+IrXqlYv8+eaKUumDhwoX33Hnnnc3AwePJnmj58uVYvnz5QX/OQPr1r3+NZcuW/banp2fBnn5XSrUlk8mtv/rVr5LAW4MnURRh3rx5Wms96K7a0tKy+qtf/erMWbNmvenXDgCcOG8eblr5v7AsG1pZJrFIUqA+t/SkIe3+oyKhW5ZVyufzYwDs0e72ZqJ8Pm+yT/eHfN/vy+fz5u83M1+01igWi/uXrUFUyufzRsJ4M/NkqFQoFKC1LuzlknIQBE4QBHAc5y3DE8dxvH1cVhKz2ZudJ8ViEY7jDie8d1AaLQn9tgkTJnzq6quvbnozxxiLivjahg0IOaXXUqoOGIu+KwxNGj0tk8n84IYbbkhPmDDh4HX6IFMYhvjRj36EH/7wh7A5BE/rGpxCxKprMd83FJ6MTaVSf/roRz869mCppG8k6u3txU033VTK5/OX+r5//2DXZbPZJ9/xjne866KLLko7zutiJR018jwPK1euLL3yyiv/N5/P/9Vg16XT6U+3tbX94/XXX5/ZV6boG5k8z8N93/p3VKImnPuxG+hLpQQxwEAkfO6jC4a0GEZlQ1dKqXQ6vcxx3JuGnztci9c2po294hvwtQPRCA3puizGPfdF1ccimmdSU4HvQ2kgUjVciJGYXPjes1tbW/6LXkGtL3rAASFPqOGs1IYlp7rBqhmIxTJUdg9ozyBcCibJXuYJJUNoA4cQaW3ekRx8pWJ+qDw5fOzYcWvCMETEeCsaqg6cZgDOTUM6vbx7wXehawzKJN9j23Yt7HS3cdW6afA+dkPiY7Ks2jPNNVHDvaqhf43PUArPlUqlr3qe983dOVF3tVKppqamrzY3Zz+mtTaLnVoX1Xxw0rIYWLNsSpJQlWJzmB/RZ1kDns94P4JvtFtfBLsENUTAAeiGWodmrg4PM4CchP39xa+USqXPaM3gQ4NQOp2+fNL0Y77kMyom93CP7Q72VRQJQigjrTLeTxgQBk8UhntmQn0jeg8/MdkO8dxNJGE7xHdBh4VSsG0HE6fOwnve/3cmLdSyVM0KwG0v+9jQTC6jsqELZTJZDdRNfvOf3clA2VgJJJIEtJVS7MmucKhZ2LgotVJQFtm2DTSs1ciHKIoAhsmNGIxLGwAdItt2YPGLECAkP/Rr9wMI6zaPkW7oADBxfIcGapu1pTW8gKV/BhhL8svNl/pp/FEEMFxnMkVhU03MIyeiSAqvwCBRYQQ1gMkGD0X6DQXNm56byjQ82w8peqFYyZuFL2RYK/CkspHux4YOAEcddZwGgF07NvF4FVSKQ9pC0sQjn2Fu5RAJq7BseveJRJKfSSp5rrkDAFCuEP/a2jsQ9u+i+2UzYPCpiLFSNDQCHlfAi74aCvAWR0M0ZaEjmmeuwPiWyYzm8VxIuAIMVq0dvg4t6J5dO4algpx3/ns1AAQCu+om4HC/eYuAJe9CXq5loeryc1kTfN+xcwEAJ2maO6sLFBXyC8/Gq3+m3IJKicYayJbKz7RtIJWkL3M2bXqJgPhqMWBaqdCNslfi+6kd8AFq2wPXo4bSAh1N7f7oh78eMl8+f88vG45ZZTmIBPPXgOU1gmlBAwh5XuZpDDu2vkafm56hzw3PAQDyO3aa2wz8Fu8tBn5Ya1g8Bovngcs8b5swBgAw9egTMG4KhRMnmymQwGZ4Z+ldKHvAfmzoo6q/1fCz6zaYwTZ0XqipZApJxnz2K4SkKIvP4KlwKJKbSCKRZvAvu3FoDc+MeFOo0qZQrTAinkhtUQDe62G7tDkoflGB4h+MgGjV/rCGtT7pFkEIZDRBOwzqQqYYqc6j/hoUOdsxG3nK4cVaocVUKZe4K4yk6CThMg63wxuJ4LcHLIV4XhlRQM/wirQ47QRNNkHITLsZaEYbDFHTThqYoWuSi2xe9gjMJh4vDoc3w0q5ivEtbQCAfM9m6oMg33Ff3EQGmsdQKrJvgp9drtK8acuRau4V+pHgFyyLKBQkPu6DYzumPRmWIBHKBu+VCgAfIq4tYXCyoOnvyBe0RQcOL9LQGtmy83h3dVPUtm1bZufWhveysdGz7KSD8eMpi37mkccBAJpS0wAAz+2gw+xPO+idv9Tto1AhHhmMf4vnu2B42zY0r4HQYQEmYIErQcKAk1FIYCfdxvwIfMHZFw2GD/+wLjFvBIB3ZhOU+gQIzVqXg0K0TIOgGil4JZojvTvXAwC2b/wjAGDL+hcAAOUdNM9a3CyQIp4kszS+VIbWk+DuW1GEkEMWywWaaxEfaF2bttP4g99Dg97b+BnUL9c5jAbh5JgBzAldM+PuTTPeE8V46DHFFFNMhwi9Lh6WelukOZQGZKIInrljhwgYa1qHjSetxRJBOkfSqu26Bs7UnMoDTjgFBcVSp8vSt5Ng6bRCp3ZQqRhs9JDNH7Yr2NwsEZuwPBj77kh8eKZ7LL0lHBuRaBAikbOkoViyS6WakRTbXJXMCr7wyCbpId1Gql46k0OqlaRbbSoWMY6zqLqVAqp9ZIIo7KIMRjFp+Jp4lHBdKDFlsGTii01axsL9rU/KiPZq3d0zNeUoTCwdkJTjaRuFPFUbCsSUkSGps9pH/dVaGzOUseUzv0SyluSXhLKgGeTcSZMpx+FEEyVmOM+rC73U3K7DfWAzAixzvVcVHtBcCHwO0pAXHPg1348lSvbwKGANMs3aiWtZ5p0YqVSwv1lDVekWZHNHAQA6Xaqu1MPWr2e7qY/ru6ndouciCIl3inkpZrbmZpIim9MuxjTTd16ZzXvcPy4ABuW0wk2yCcJoxqxl+2K6ZNu1joxN3h1BlEcoNiEZr9JGu5BqTjUbP2tIgUapQBpt1/Y/AQC2byBTS6mbJOpcjtZRpmk8UtmJAIBULtPQrsSq60ibykTFXlqP/V1kuooqpKn0bt+M9davAACJFI23k+ceLHpWxHuAgjImMzVMmTuW0GOKKaaYDhEaXQldqvyYIgV1P7EUpVkySLjkEdZBEVFQk4gAwE6QTSvF4UoO29C1suoiYAZEwtQ5Pk09Qan/l2CtwFQZsf4fe18aZllVnf2e6c41dlU13fRANyAooGKYNBBxZlCjxmgcYngSvs98+SIxTk9Ev4DiEIkDGMEhihrlUTED2opBnBIjyKggUws0TdNjdc11xzPt78d61z63ip5uCU3SnvU8cLuq7j1nn3332edda73rXYgZr9fuRuB7fY4rSdscUZZy7DXeBUgPUTswSP9K9UBU2MowK65x2lq5hqQpT/5QkXkgCGpwTBIu1UFB6IVCgGhRkYKT6vVzPiuDqDIDXyjKueZ2S9VbxH6rsQMEZEfYTj7a8YnHscwGmCWXLgNANP0wAGBqbhYA0GknSOlNGb4mjIuXGE8OO7HtTGPrURxltcjPPufTDdvwSoKK3KrMmyao0rYgrCQ18NQDiTXeLof1+X0Uyn2ImIex65eIPVsVjBV39cZdqlKEhskTnqPkleDR27RddBT5ElmboA99y1gx7Ignu2tcUOjO3eyy1NZjeEh4kTpnnqferMxzpVzE8lGZs6DNZP2U/G27lsCnfYAva8Mzco6AyWz1shVFF3yPctRAmuyT1LJHU1Df7R3bOL1liymSJsEhijA5KR7fo5tlrbWmZa2N1CSBXh6Q+weFEtIiewWDSWCdc/awTR0gSuRvXp+8t5YwFzEl15a0HbQm5Ry7Ht4MABgafqq8d4znsvm07i5oeQw9t9xyy+230p7UKgXXcWwsTc0h3cf3GN/thDaeZMjSCMhkSRm9q89Llt44flcvTEUCcryS0t4Q2V6iMTuQa7yxUGSWvlRGSgZIEjKmSvSQesoisC277TmXVAaj2Wx2UI+crDOp8lV9jqvAc3rpHKK2MjDkb9Vlos9c7R+R97ITu0kjxErJ4/ObxBj4jKFHUZjRMMtCbasKUEF9p1AHoziCSYjiGb+P6L3YiLCFSW5G5VwCUveqMoZ0SrwQ3/MsI6fWPyZjIFqan5mQ87guPCg6lVf1pvo4f2lTEJLxCvDLRFAVenlcAyq05CSpZVB4vE7NFaQemU9xy3opxTK53fRwWrG8JkSfhaBoe4m6S6wILDDGqmwZxzOIYkHAcaRxfhmHF0iOodY3Br8k53t4t7Qu3TwuczfXkuN0mCdKjEFKZB5znQ+V5VoHyIsc7StiuCw/jBLFNgqk8fH6dk6GiFwyQHzJS/kBPUnNUcSZN6dMr6U4dcqC1H3E7fKEsnoE9XjlvY25OqYelXXd3M01ph54QTRpOqmsi5npSUSgt8pzHj4qa3CoKu9pzNcxMSVeT8mX9TNUkbxV36DI5DanfbQ7u/lvWYeNKfESqkNMwPi6wpwuVl5vu0qO0HPLLbfcDhE7qAhdY9fdcSEL6ohafEUviTztJeZGRBJInLNQ1LipxFE7jCPDKdkefNptPdGCGb/Kz9TRITrwNK7ukI9NlFwuB5b1kGpWXhkhLEjQGGNs0t8IodvPaFVjEFjGS0KvoE36QH9VvIyk1bLv98sSzywQSc3MbAQAzM2RpRIbOCpTQEQxPCSx1IjMkUazjTgh37xPzlGtCNIr9wsKS2cmbC7D1S7xRCOteGHs00GGtpbS4cnlNYGsEgdF+PyOClVBPn5HUI5L963o+4joTbkBPRr6DmFL4uIux+nXhuBV+vheLSTimmCBljExXBYSKX8/4VrQApjKwChaHfLiSXNp8XIj8q4N102hvAKViMis013ZeODmMzfQbPFcsbGFS9oIIeXYXEd+31caQqMh1zY+I5/b3pafW4yzJ8xbOa6DNtfKyGpBoc8+ShgeRxDlVysBqiWZjyFXztnUOQzJfmp0sIP3ZkwGh+8JY8nxZF3qvZea1ObUkr1rce3dlHGkx3OzPUU9e83thPw+52Z3oD0tLJSAfPGAXkaB677JrbE4MILAk8/PtYQZMz0nXmE4K/dPJ4xR5hrx6SG1QhnEYFn2rEpcRacj7w+pQdOZ3c1LkHWRFrRStJqxlnrcVQ7uhr6olF2oZkr548XoZkb3TXx2JmZIU4SrYRUtLCJ9sbwMAQshOi0JwzSVxsWbO4kN/ILczLUSqyFDJt/oHndSD3101x1Xq1KVAtXt2un4Fo6nF1tcIu57HiK7QXK+mITxGEbqhDHgytg9btKFkrxnnklMTeoMFgrwWDE5ybXfYMWkx/rC0tAgRvmQ3LVbXMeQCdACHyJ+fRoRy8JdUhl93QiYHLLJJ5NmV7aEmEvKzdEmjBHCcXjOioSUvFhujgo3/3q7BU8LqDR5qUl4FggpzdUr98HjjZZaCQBuKraaswT4Mg6tQHW1kARZVWgQyPyXKzKXbYKLWBtaqaZNZwotnkurcXs1DSFptSwSDxFP5Bc0bCE/l3jPVIMaYhYu756QsTU7vO1dxlFShucCH0+nHPJJT5fXkYp8po9xuqk4RJ3fe8mRz2vx3URD3jsdpQg9jpVFRx6LalSLxmEAI0mNDYW46RIgkYZwdCtLs8bKeovq9+UxPOVGU3AjoVx6BH5BkcCoKutirimfmWskgCZBNe/syPxHBJphHGJomVBtTSLX2ZqTUJMp8TsvBnAZQtVCxuacbOhJKGMxDNsZlIA9gN8DsTzkkltuueV2iNhBTopqmTJ/dJzHIjh1nSyyzpC962ZJAwAIioKOagUt23aQapKIYZUC6WiuFhsYB666NoRyBSPT0OmQPmXcLNuij/tkoWiRY3QsydIyf/ZyF4prtdthpk2jBVRa5s4xJImxmjU+1Ss9FpgMDAtScCBhFde00WTSuETPxPfEHTaknbm1ZTBEHVYxQYErO/q4hQLSDhGreimLvo8sIZ0lppYShxqfkOTdAL2D6bkZlKr0GBzxQGbotpbpV0dJglJZEoF+JH8r2vQyw3lE80Gphrgjnld9VtznTsSQCyUPisUCCixuc6nkqvRYLY7xvCIcl2JOnBuf31VLUbS6/U4Frpnl5/enDLtna7K8POA50ihCZEMt9OK4HlyONQ5jzLe4tkL1oFh4pclCJgTL1QqOXi0e0GqG02anZH7uHGfhWVDG6GpJwA9SDqO1W65r25Tce7vrBhHPUWHfAI9hGY9egZIeUuNDaQBW96UHi+nNOQx5GKSZvo96avwSmg164jO7kbRVupprhCjc0BNJWfzViiJEDXYiKjEEV2G4lfMXew4cerR+LPdLx1CmRKUJfNd62im//4jfp163UglMChsvSvcpRPhYyxF6brnlltshYgcZoWu8XNX5ust/GOeyT6buj/FzNjimyYOsVBYAnDRBk+p5StELVHXRiR57TqJiTwn9SnuDsfK4RkWJbNHR4oIl0/XP3ku6szgfUcoCbVy+2NLl7Pnr2rErhU0QUNkXyp/GbqPGHFpEAMUahcuIckON+RujVdFw3IW5Ai1qclzvMZK8rpt5RvJ7RV0ZLHeXILgUtrXfg4zT8zy06Z34qqjK2GnMXEKhVIPLeKjGzjWhHbDEWrvAzM6Oo9PR5tCkghJhudYxczMxOL1uLbJhcD9uztnSc4fUSDAZqNTEVGUWEMNXNc6kdyQKAF5BxdWUOmsQcGlo2iUKZWxtxtYn5utohTW+RxG6MgeICIn4y31l9PdxPpjDenizeEvb5wVVHnH4MgzWKYJGr/eBrZLP2EGE3klcm9BWKQT4Kl4m16CqhOXAs3TTMOo9WdwhyvWLjM37Pgy9a5WdsGvZKoAaWwSov1PSQ2SVWBn7dlO9FJuAX1zEBXQV7dmafT0+9zPHPEaGucPvqtmS1z5HE/JdBZI9Ov85Qs8tt9xyO0TsyYmh62s3DNcCgYUgEFkLgYyGZNkUiz8bh5lAP2mKWrKsIkpOV4ch/aCrtEhLzOjqxr1Y63tR4wc5RlYk0aspqrc5A7cIlwijyJh3mdnvhMjVdWDlUq3utaJiK0jEXEKriYhsBG1sYGKNN8b23CrG5Hahdnnp9ki0YGPh2LFInlTmgcdZglSso3FXovJW2LBTPM2Yd6km8fKUlMSgEMCjzHCRbJCAFMfUZ6xTQU8cWe161TwPFL2RxjbXaljPqKDfh6uFWcwhGBexrzF3OWebdFv1DvQ8Udi2ZfVL7cRkRacU9HqeyowDZB4lhnRb/r7daiJJWAjF212/0oxZJb+v9ZdRCIi+Z6mD36QgGY/bbBhMQeZ857ig9627JB7dCFVCIMoWJmPmAfMR6gpGHdJuXd8yX0LVuO/BlLKc0NsolqswmgjiWtY8A1hE5tWGYDz53iwbSYsNmQtJWjIPVd9HQFGuea61TlPeU1Txt9TA5dhTW8THewtKf45tEZrOewydL4088GfTxQR0evNwc4SeW2655XaI2MFtcGH/0a1ItRAZ2s49TvYpRTk2Y6xxr0Wx5iiJEBvlrJM7rIAzeawgmMbZI1sX0xUT1tiXohlLNV/Epd9X/6kDMMuascJNkX0qh5TGNWRMFGwuwbHSBNp+S5GBNpRIiBjidgRDgX6XxRMOEYEltKQpTLowvqpzrgUzJo4zZGnTCcr4sRPAF7erRqD3HrJJUwo4EjfzglRCuMqS8jqRlHadctIEBfX8GFfXxgQ+EbbHNVGIYwRkPLTYBWp+hkUeXI+FUgUDg6J/4Ch/OV6IutMoBBjTV2kC9S4cqJasxqu7UOuSitxhJRy0bN7zYfMqyoDylISvQldxAk+7DdmbShc1G2ZQ6rZQyErCVChL5VvVu9gxOY1Htgsin6MwWb2zUHKhq0eekkdQKsi8xpQZ6GiTkNBYXna707tqWcSchX4nxWIBJtW8z0KvWvMtkduH2GVegTF905HrjZpke/F6CwUDl2vOkrgMmTq2ICtGY148R8/RjmkLZQfidmzzLQGL1yp94oFX+4WRpnF9B8Y2++iV5XKQOxbJq+0u0l2UY/XLNXGqQ4ugHDpDnXCjIRK9gbnhxVHT6qjrpKlORBbdsWmaLFliR5BpQBjNMtneiguTHgv01n+DZsaatFUd6yRNkdAPjOm2qcsYq8qfH8CoQh07r8QhC25I39KNvhOlCFh85DH0oAUqHVduhubcLhi+J2a1HyViYHjTJnGUbeT60LBhC9XNyZTRlToG7Z/Yg+lshty4UseDz+O16ApHfEBXePzUAODGC6MKmdzIqa/SrMtcterTmJuTYo5U1QULrIxlN5pisWIT5w70NgAAIABJREFUWtoyTjcIsFDJ9wO41FBR3z1hYta1IToOxUgyDrCFwD2bsdWVHEdibKJOw18e2zRqVW87iuAE1KdxtJpRxybrq0YaXsk3CPkAD7Ugyizc4NtxjDBk5aVqwGvYwobZjN3QWFSKKm/nWYbFElbShkkHqVbwknrbi8UMg6gyphNVsrUXKMBgtTg32/7+5ZgalArYZPxhGTKTwD7RXYX7yMT0BJJZzinnbWxE9Fn6Kiwias6hXhdgUHBkDVbYlyDrZNSy4cuAVd3FPqXR8r12j0rt/uf0WGyVh1xyyy233A4RO6gI3Xam58+Cchcn4eTVJw0vcRIoQm+TRuaVBT5aihCf8GEUwSlQ59qeQ0z1OFw3RkRZgE6kiRomUm2Rj7HJsSzmQs9BiwO0X6Exj6Fe9mJK69NOPNLBRbvG8E2sOTbULEdQAjpM4rApcbsuIYQi1eJa1DVp+2WrIqlozguIQqtyjc3ZWWzeKZ8vVeVvVbruPkMSYRTD9bVjCymRqSZ06XJzHh3Ps+69vwTnpaA6K4mGjRL4LBArDwgt02O3nLjBfqtRC0ZL9Blq0XVim4IzLGKcADVq1BgWaBWIbFU3J04iS10rUZlRtcFjX/W7m/DoNQYsSAqjhdS07DW2X2ihPND7pADWc7OegoFdl6qLrh5lTA/N+E14LqUPHO1/ysIYejlxqOG1ONMkdxaGOSNF7iHQ0fBEurCRMTiHnpuA0T34Gh5lWCXhZzuqFmocGzZykt7xZYOdtjzuKo2ZcRTY+zNgVyuHBAlXyQF9fRhcLgh9ahd1jaiA6HTkeP1DIxxfGX6FHdGovV+kZEhC+mmpVkDqsUeDRiGapHDWJRQTd+bhsQdr36Acb3i5FAEWKFnRtHIZiawXZFGEA7UcoeeWW265HSL2pNAWu7tbO11xa/kdY9NEE8Z14WjZPTuoR+xsH+iTkseI4KLK2LlqR9sAHxOKpUoFRsVx2nIcRdg1UuGCNLQ9OrNEH5O3iqi1A8uC6+sdjjqqr84AWuAFFhlqosfVuCgRQeoX4JCOmbBApjmzA0DWTSegHvbQsgpcUuocQ2RJpFLpZ6/E2vKMSsr4cGdOKGn1GSkaSRHAo+BSbBifV89hcVLUGKtM6Hm9l7m7MWP7FBqDX7K5lVZDtKlVKMvvKmYKakJT7F++DgCQUAJAdaYr/eK9FNMUEUu/Gw2ZP4vMmZ8JjIMy8wqK0FMV+SK1L4oiuP5CBKseXUZN1MRxpnHd6fROzwMyFGpzmsbYIhfjqnfJ9a7eSNiE57c4DXJvlAifFYxr0tgxBgWu76L2s9WEIhOynShFZJOetvKt+yf4HlDWwiF6B9oJKlVaMdd7OwxtvDhdgtri1MQu/ota8S0HlVTWaY27m3b6clV2ICiiyp67g4eJxzfRlvtnYlZel1GYbWRwNcAOaU5RlTnFUpsAB2oFWXvhHNdTJK+dUK67lTbQPyz35Ni6tTK+ZaJoGZPKaXN7bmojAL3uKDlCzy233HI7ROzJQehWGrJbnEuLjbQIRBkGni3ztuXejBs7NiYsT97hwRGLpPVvWf9SIq9iH3yKeqW2vyELLpjpjhotpIo+tTRc2Sgcrca8TVeH7l4pRgAwwqf0+E7RZ/Y9F51Q2R0ssNEWQ0TYceJaHW+H40yYF6hPbgYAVAfZwWjQg7G9VtRD0p8ce20JKZLRnIyjOUFJT86bVyhCu9WHZCjYBLyVTuBxjYFDpBh1tIz/wE09CJ/sjDhJ4NErSCgwBcaIHWWcFKrwGOt0NEbcYed5/hxQT99NQziUOY14joR/q9Cb6av12zyAMqRUbjgNdHwBYl2biVL/tHiLyFQ5sa5jWVm+ougezd4pShd1XbTJSgIRcZHMDtfVzkAtJJSHLmoM2F8oF1Eok6rpQprHAvD4HbRblLlN7JdtxbQyOWxdr/JaLhawZpDjCcU7mG2Kp9dWhN7FVNO8mYfe5yUKZS+Ynx8HAAyNLEOJrBFPi9oW9Rn2Cp5F6APLpa9npyHjaux+EAAwNUEaa9OFV2FEgEJ1GqOPLG03RbtOZD5HT4QS1W16m+WhZVi+7plyzsNOAAAkgUhSqBp4d69lu+Z6lOTOEXpuueWW2yFiBxehm8X/6Ba3WVj0kJA94LgBHKVKEEHHZKAov6RYkSddUKhkxQRmcfTJVgbZoiV9+kUtdvdpUd60085K6xkncwNlBmjcuItHo4JOS2hwETN26xI1FV0XUYc5AR2nxjX52my34JJLXigKsvRYYJE0BFnPsatNe24XAvJdixVlB2kslii61UA4J9ferk8vGJ92/3GDAsKUKFmbCiiMsPkPfqiri5O7BMGygCwV5e3GcRuG6DiwtQdaOKPsoCYwvhkAMDO5jWNmHoXfYUBk7foFOMpgUcEr5bOrGBlS23gBXd4YkBUopWFgY/kqP+wkC6VQbVd6v2jXb7qEXAsA28wiilWSFQg5yEDXvQ6a8rSOiZBGrFVQyWUKWVX1OnjctB2h05R53T4u66BO3r96zMYktnjNssyM8sjldbAWYiCV4rAm+f5NslpCmx9iYw7PtaJ7/hLqOeKYPHSXzBbfsV6+FvVoIaJ6AK7joNYnMe/lhwtaTjvKeJP3ztMbHq/Pw2u27VgBwFWpXZsmSS2XXmszlJnXNyx70/IjjsbIiuMAAOWa5Hi00Y7mVuz+4ThwLfuvtzk5uB2LrDqfmrHjzYp6FhIOU5PaLkZaoQVNDjIR1U5k8cRBCwWlqqk+ss21anGFyRo+8/MdTb5p8hXZDa4a2EpRCrVtiQ0boauErPcF2WRSs6CFUEmCIFDNjYVVf21qdsdJmFWqMkRQKquSIl19PvSiuSaieaFiNd1FdEhuzIlJs7ARN05taedw7qMkRJPJPA032QJeHi6bBsf+MVnCnBT44NBCjtDxM+oh6ZpQ7WxLnYyRMGmua8jRxBJ/7mjxCRxLUdXxpZaeSvqbkz3w7QPQzp9+JskS2DaamCUYAcDTZHqhBpeVmfGixugHahoGtD0D4ux7s51tFhUCpU4Il8UufkIlyJStHFk9FjBc44chNm1+BAAQNWR+m8niB3OCVEMDnJYq98IVJZnDWrwL821JVs415GHSibSFoyoVdtEi+avOEuZF56SptN1SGWBRUJkhpQrpvgGTwnBSG0LtZztGZ/16AIBX1PCKfHZm106EM7K/aMVwEso8aiFVahwb9tWuWNVRCekctmYNAGBsxZHoX3a4jJH36uKrNd3h5yWEb4E85JJbbrnldsjYQU6K0jJ2217/qE+rxMQwqmpmS7DpSsfaW5HlxEmEFkMY+h53kX6FQWrRutICNbyiCVDX96wCnCKwWBGPRRGP1Ybptf8fkDXoNYmgSy81METO6j6rIqDb3SxRNURYbNVhUs9nb8SgyBBRO4QTL0RtZvE4HQdaCVIq9fOX8p55hnKiuGPDWMYqOy4sFLNSDN2TsoRuTtosWJX0jAGcQK5Lwx3oY5JbE3JJDBOrljWRuH5nRHGuJpBhsgaRC/PEVmHRNbBznNrqoIXIVD6wSBPeWxg+UBrkQP8AZneLp6Q9Sns1LSxSDSI4WYNlo3IR1iPVcXVsqb9J2RuABUG+vsZMfIcdGPV+Cf1VWbKsCN13rPSDS32W/op8pkZUbpo7MdeUgpqG7bGqc6j9CXT+Y8SUsVgKKDU2FESPMIkQR2xQbYsDY30zX7MwoMph9A2y36x3DABgaEDICs3lE5glYaEzvRUAELUkwZsw6d6OHAwuk0KkvjH53LJVQk0cHJPioVJtCJ6GEl3VYF8Yask8XYOMm5p3LMott9xy+620Jweh01zX3UN6SJ9e8pPjZAU1iaJupfMV+HSOtIO4Y5++ro2t6nH3hBSJ9IlgPJs88xHGShHUZMdClKxIfana1mpaPFNSNp5JbEl9rJ4ENCHbpbu+CM60iaQBxuTpzZQrQygXFnbjscUuNrmVotFWOQQmw+q8bhVJ64alVtSMaFYRoD2ui0zSoXfYlS6mm5lMQmCexWCqINiaozIjAK8ihUNrBiVGuXlcqGcDVfZ51L6fQQFgp3qH6NDTPE13FyxF65rP0UKvbg9K5yJWrWtFW3rd9CQKZbgqXub3rkDZfX6bRHNdmzC3tF/blUcTgSkKnLwi+4QGXCNOKB6D9khNozJK6gER8RcZa27TCy6WSzbR6zMnkBKxdpoy353WLNranzfVXILSf9VbyG5wl7TcJF7CWlGpEHqYru/D4f7gMwms/Q7U0UqNsb0AsCh5rQqIOg+Ba2yBWhTLWjOJ3CuGMg9BuYhin6y5vkHxcPv6BI2Xy9qTt4iUe4exAn8cz6JrMo5jvV+3x/0lR+i55ZZbboeIHWSEvrDM36RZLMssRo1YGJ8Fsgx5ApY1E1VZhgiAREueyYrIkv9acOHAQGPy7OGnYJIFOM12s6sz0eKY+cInpoGxj35nCbRFZWvoTESALa02tnuQnkyZP+Yx/TwVJSdaos2Cjla7ZbvGZMVRC6mEJoktFVH1xW1ewcmoXjZGbj+ZFZssHOhv5rXEROgF9VoSoEO2hE+hsdACrKyoqUAPa5rl/A4RWpW65rNbHpKfAXQ6C1ky+j3rGiuVCmiRzmpRu1mIkAHHFqUpwlf0lYRKZ5TPzM7tzuZ2iSyXxXTf1Fhym6USGuttEpV7DiqcyAoF1zxX7hGVbi5R+CwIOmizQC1tK8JWKQR2GIoKCNQzZncmL6aERsLXNEZg7x8ythg3LjI35bJgrdPqICQ7qVzq3XOxPVtVvsAtwfOlxL7AcnyP3Ym6u/9k/QfU+9JCOPF06xMiMbF76wOYmZTY+fyUeCAhGTW6CP1iCdMzmsNgqT+LuYbp+Q6MrUWhT+LrxrcNfDka/T6R/V5zaz3eSzlCzy233HI7ROzg8tC7SlsBCHneWYhWzCI0upAkoXE3edX4tsaaHRcIiACU/5t1Fup6GvIw+vkoVIGnLJq1KNycIf3FtAgDixLNEhgdGhft2Ook17IIbEMD673oqZ3sXJb3zI/bwqyMH63l+xqHtuyeNOMGp4u45fZcNpbn2SYKltuPRa8mG2cmG9v7nGh/xpb24IRjmT7KXOgfENQ915SYv+t6iMkTLqjUAZkxy/sFsW3nUNphZAuztGReG0Lo1xCGkUXSZcZHbd1Dl3CbsjXUkwnoQWiPykiLozp1WwTkKvuiR7P5oK56gtQW6AgKrVYlhjtQk+vrLwVdgnX01FSkilLTVcZ5fThosyApLLJjTrzQA/Q8F6zFguupZAaPF5f5mRDtRT1bbaGZrjPOQadVREhPaJbNIHqaE44hUXGuYg1BUXIpTkBWCVlFTpc8tiJzvQc6DUHSkzs3AwB2PHA3AGBu58NoNplr6GiehKwgIu2o1UCbRYn1GZEgmB4XhD+zW5g/q9Y3MLZG5tDvF6QOSi7D5ql0TJmseK93T47Qc8stt9wOETu4DS40VsSfjbN3BNddeKfiNVkl9sK8sAXLCdCxf8vQ3YJ3GWOfyqlF296C4wAZWs+YIIuuQaUBuuHoEkzHpzRdJ82iZiqX6nsLmQxJmtpYubGc2sXk/oyzbq9BJVb1nLEiApNVeS6qhrQeTtc1OovienuKCXc7ML2ap0wnK+CUZp4ID9xmT9C0202gYFdMOYS0Lej93ofq/KxcfzEIEFqBKZ5T59jOa2rnIGI8ebEoFUzXOuHXELmMR3uC5rWJROACba2tUNmCHs0K1tm16Nr5LfGajxwR9Le8LNc64KSo8NsIuGbtuuK9EoQZMtZ2jjbGTDie8EwFFyioXK7Ntyzysl3PVpNmvXmVJ67VyfLnuuMjZNu/h+PepZZLFGTz2dilVFkGvyQeR6p9OflemwNJU+vZtNnAZXyb5Fcevu82AMDcdvm5GMXwuW4qVfH0tBLVL8ucR62WlStWGeX2vHgdYWuLjCEMLR9+ZL0IghUooOe62oIui6U7loHXG+Z+UppEO92/6d65gSzBaN+S2i9/sYufHaZ70+amtWge9MsUypKeamGSNnsI7Gkb2nOIo7svaq8JDCDThumWPLDjsmNnuKGr76gdVVeYAwAcVws39LOA0f6EVitCXlL7cDJdDaA12bqQBmeQdTxybcIG9vMLDozu77r3OZmrqz4Hi3O6Sqs12Z0oPZVfdGISm2hrU3tb72RNxEWk67UT147VdvqxYS3ddLOHiO0paot1WAjjZBeaco59q5OvM6D6K4Us6LfUnPGidSpfCqUaqNVdJPpxmBguRCGKVtOERU+kTfpFhoA8ndPU6p0UtTCK86t6nR6616UOh92zVGrBAL7VU9cHHpOscdZjEwBKxketT8Yz7vUeMOinjtPggGj7lyuDGUCzoctF8gwGViZielx0f7ZsvFN+3vqAXKdhWC2ooG9IeoiW+rihezbmJK9pYnsQd9irYXZaQi8dqkGO79pqyRxeSca1kmN3HXlApE4GuOwDsMcEeh5yyS233HI7ROxJSYra5GMXus1yjfqejFqXLkoA7uHA9hhZMm9vozAWSWeARzOBOqzsPe5eXMcMUWcnWkqNkUX1WgjiullRiiZv07T7R55HPZGFc+rYLvD66nTNpSaHFMHoMYxFNYooF8siuK4HhxQ8x2TIfsFALap3M3R0oBPRZZXRI+SzM+KuRnFsu9koErIhEnVFkgzVqCehY+gw2WbHksbQgh+VBdDitczjybCOR5Ra7hf3vrOrzuNlVE4VAoup/27seukOsywOi/VoVsmO53QdK4uRcIzhItGyYpqiGJLCyCRopSbCUYUBQYgxlQVLlQoSFl8VuAZjIn4t6gtcL+tzmWXp5aWgfXuBlLRNn31CXVIcGzNSRq8dxbyOa5VMC4Xe8eUAk8BasNRsNVGskqaoIUrr9atom4OZKQnHPfLgPQCAme2bAABlvb99OcbAsuUoDUsYq23vl4z+KMf1kFLZs1iUOajxLfOTXLftOuYmJbm642EJ5wwOinBX7TBJ3qek2TpuYMN7eWFRbrnllttvqTlLoZXllltuueX2389yhJ5bbrnldohYvqHnlltuuR0ilm/oueWWW26HiOUbem655ZbbIWL5hp5bbrnldohYvqHnlltuuR0ilm/oueWWW26HiOUbem655ZbbIWL5hp5bbrnldohYvqHnlltuuR0ilm/oueWWW26HiOUbem655ZbbIWIHVT73DS95lgGAuVgkRjvGgeewz6Ev0p4Ou4GUA3ZV6dTRpEj8HCU5E/ZrDCiBOsfOO6kTYFh7C1I6s67SnuwdWSvVkLYp3s/ekPW2HN+rsMdfpQivIlKWPkX3Ox12WuEYvEgkR8tRC452PY9EsvQH9871onlJdTRtIdD1lVAmde5RkfjsX/M0Xowr7ZkAPHjz9wEA6096EYCsGcMTYd/5NznXP1/7jwCA+XnOyaLuSSlc2xUpotztddd994DnZGR4bEHj1sSk8EsiYbpm9TMAADPTGwEA05NbeO7Hdl3SphhBQXpLVgekUUHfwDAGB0SydM2IyK92JqSz+613/xIA0AgjpGxo0SlIB5yjjjkGAFCYmgAAbN/+CFray5XytSkbcDi+NFwYXnWS/L2wA51JkY5NHTn3pnv/qydt1JGRwxZqTZvUSsMG7JFacCm9zD6aK4ZL2D7B/p2d9oIxJsRzBfZB9T2g4Mn91+405BQqI5xo/9kU0IYfifZh1f6XlICGm/X5tfKvPI42m1B5atex8sQOP7NzYvyA5+VVZ73AAIA7JZ2HdrbqGKpJI4pnrR0BAFx3210yF2tENvjIahkzyRAA4MHZ7QCAIu/dmM1RTlov3/W943NY3yfHGaKU8K8m5TOdWPaNl/7+K/Dlf/kuAGCMa6w+LevpBctkPr+3s4W0wD2twF61vNdnKW/8vJN+HwDwy1/9Kw6vHgYAmKAc87d/ftsBzUmO0HPLLbfcDhE7aAjdcZxTn/W0I5CkKeJUkEKUpkgjCunHHApbahUpdh84CQz79SlKaFPoP2LX7bDD9lYJsENF7G3/UiIYPrr6Sm3ECRs86Cm1nVlbkExzso5Uu8GzR6AxgheXjQxiaLCChA0XYuPCK4l3UWT39B7mZPXHPvYxTE9Pw0RswhA34ZbZ6movz1vTmEerKeigMipIEN+9qadz92obH3wYv777dgBAGGqnezaxSBf2iuUo5f9G/uc4zl8C+KoxZmZf53Ecp1Sp1B7bNzYUdLvjUfl4fV6uP0qyRihZAxCiv5At6VpEki5bfaXzmN4l3tmme4gkiVpn5uT4cZJkzUfYc3Py0V/Lzw1BTXONOUTaSMQ2TeGrtxWlUg3bJ6V7/MiqEZxw4nPk83FxX1OwR3Mc56Ul9sp0vaz1mbolrWaD7+P659Bnpt3umQEAJNoLVH9mOzY5VtYKUX618Pq65ba1uQoW/c3pbiLDd9imNvZ6ulsu6vF07M4LjTE/2Md06PueccYzjkEUJ3Ah1zQ1E2JiStbG9voueWMs535gizSY8A9/Ku5+6BYAQOTK9z/Xks8PFMV72j79KwBAteBhdm4SAFCk97J1ku3luAdMfO2b2DYhDTNOWi8e38Y5+T5ubcveMNOYQ9SQNXYYe5PWRsRLuOUBWVeJ988AgELiYJzNXSbmJuHItb7YGPP9/c7JwdBDdxzn3Eqlcs2rXvWqSrVafcLP90RZq9nEt771Law6vA8jw3JzOVELni9zqBv6d3/w6/26R47jrC2VSreeddZZo2NjY12u6aFnxhjce++97TvuuGNTs9l8jjFmdk/vcxynUKlUvn/kkUc+97TTTrM3+P9Em5iYwHevuw7e8FHwilXZ0A+XpsC6oV97zWUH9KWXSqX39Pf3v/vcc8+tFou9Pwz+J1mr1cK3v/3tZr1ef1sURZ/d2/scxzmjVCr9+ytf+YpKPztJHarWbrd1Tt4VhuEV+3rvQdnQ+/v7f33JJZccffrppz/h53qi7Ze//CXe+fa/xrOOl5ju9OwcZluC9pySxDEfuHv3fm/UIAguffWrX/32d7zjHf9zd60ezBiDv/iLv2jeeuut/9cY86U9vcdxnOevXr36W9/85jdrvn9Q0ztPiF199dX4p6//GwbWHI+jjj0Zh5XkmibHBf1951+/diAPft/zvNaGDRv8sbGxJ3bA/03swQcfxJ/8yZ/Mt9vt/r29Z2Bg4Ma3ve1tz37pS196MIf2pNnmzZvxute9rhlFUc3sY9M+KHdNkiRD69at2+d7THfn+UW2YcMGDA4O4owzzgAgF/flL38ZALB27Vqcd955+OxnP4sXv/jF+NrXvobdu3djfn4e69evx5ve9CasWrVqv+dYbJs2bcJXvvIVAMD69evxx3/8x/bfrU6IZewd2PE9tJiQcmt9B3RsACiVSoevX79+v5t5L/MCAB/84Adx11134Rvf+AYA4L/+67/wne98BwDwile8AqeddhouvvhinHvuubjhhhtw22234cQTT8To6Cj+/M//HID0Te0FHd911124+uqrsWrVKrzlLW8BAHzkIx9BGIZoNBq46KKLUC6XcfTRRwe33nrr6D4ONbJmzZp0f5t5L3MyOTmJz35WgN7PfvYzfOMb38CPf/xjrFixAv/5n/+JqakpbNq0Cccffzxe/vKX4/jjj9/vORZbGIb46Ec/CgC4+eabccUVV2DVqlVYt24dSsUSjjn6VJTjDnZvexgAsGPikQM6Lq3P9/10f5v5b7pOfvKTn+DGG2/Erl27cP755+OEE044KOvksssuw+zsLDqdDt7//vfD932sW7cOnU6ntp/DjT6eewrw2Dn50pe+hG3btmFmZgbveMc7sHz5clx88cU4/fTTccstt+Dmm2/GqaeeinXr1uF1r3sdgN7n5KqrrsKOHTswMzODd73rXSgWi/jEJz6BnTt34tOf/rR939q1axFFUQUSE0v2dryDBYMcndgNGzbgtttuw7p16+B5HjZs2IBzzjkHz3ve8/Ctb30LURQhSRK8/e1vx5VXXgljDB555BG84hWvwK233opdu3bhpS99KS666CIAwDvf+U57kmKxiAsvvBC33XYbHnroIbz2ta/Fo48+ivPPPx+nn346Vq5cibm5Obz61a/Gu9/9bnz4wx/Gv//7v+Oee+5BvV7H61//ekxOTmJ6ehpnn332Hs/xeIVGHMdZcKzHa17e85734F3vepc97u23346//uu/hud5+PKXv4zTTjsNAHDyySfj5JNPxsUXX4y3v/3tqFQquPLKK9FqtfCUpzwFt9xyC9773vciSRJ87GMfw7vf/W5cccUViOMYaZri7W9/Oz784Q/jne98J57+9KdjaGgI1157rT3v7OwsPvShD+Hzn/88tm3bhqOOOuqA5u6JmJMLL7wQU1NT6HQ6qNWyfeJtb3sbtm/fjmuuuQZvfetbEccxXvva1+Lss8/GC17wAlx77bV4y1vegssuuwyve93rsGnTJtx4441ot9t40YtehJGREfznf/4nzjvvPFx44YUIwxAXXnihBRCO4+BxCqRZRPZErZMzzzwTZ555Ju6//37ccsstOOGEEwA88evkrW99KwDgox/9KObm5jA8PHzA99jjvacsnpPzzjsPAPDjH/8Yt912G84991wAwAtf+EK88IUvxLve9S5ceOGFAID/9//+H9asWYOjjjoK3/ve93DppZfiwQcfxE9+8hO85jWvwec+9zkAQF9fH9785jfjoosuwvve9z786Z/+KQDgBz/4AW6//XacddZZuOiiixaMQ6/VcZx9gXMATxLL5dRTT8V5552He++9FyMjIzjvvPOwdetW7NixA7VaDa1WC+Pj45icnMQFF1ywYHF1u1jXX389Tj311P2e78gjj8R5552HgYHHxtquueYa1Go1DA8P45577sFpp52Gs88+2/79uuuuw3Oe85wFn3EAlIseykUP1ZKHkdEqRkarWHPUaqw5avUSZ+Xxm5due/GLX4y/+Zu/wTve8Q4eMX1UAAAgAElEQVS8/OUv3+8YXvKSl+BlL3vZY36v6K1Wq6HRaGBiYgLvec97UCgU9nicFStW4IILLsB9992H/SGpfdnjNScbNmzY6xx12/Lly+2NvNi+/vWvo1arYWRkBHfffTfWr1+/4L0/+tGPcOaZZy74jOMWUSgfAcfv4PBVVRy+qopVq9di1eq1Pc+F2hOxTgDgq1/9Kj7+8Y8/Zr3vyR6vdTI+Po4PfvCDmJ6eRn//XiMs+7Unak6azSZuuOGGx3yve7JXvepVeN7znveY31933XWIogh9fX3YsmUL4jjG+973Pvv3RqOBH/3oR/i93/u93i98kT0pgcqEzIQ4jqFJUmMMnvGMZ+CP/uiPAAD1eh0BecR7WgzXX389duzYsdebr9sUlRUKBXvudluYC5VKBW9+85v3+LnrrrsOU1NTeOMb39jD1S3dHo95WWxf/OIXLTq4+OKL8cEPfnCf79e5CoIASZKgxfxAmqY48cQT8Yd/+If7PefExATm5ubwyU9+Et/5zndw4403LnBte7HHY06MMbj99tvxpje9ab/n03Po9QOwc2CMwfnnnw9PWSaL7IYbbtjv/D4e9kSsEwB44xvfiHPOOQf/8A//YL3TvdnjsU4AYGxsDO95z3tw1VVX4e6778Yzn/nMA/rcYnsi5qRer+MjH/kILrjgAhwImUPnREMuuscYY3DGGWdgTznE+fl5XHrppfirv/orVCq9seT2ZE8KQv/5z3+Oj3/84zjuuOOs23TaaafhgQcewOWXX44PfOADCIIAIyMj+OpXv4pf/OIXAIBbbrkFGzZswP3334/LL78c27dvx8c//vEDPu+xxx5r43g7d+4EIAj2gx/8ID760Y/i5ptvxk033YTrrrsO9957L6644gps2bIFl1122YLjGBi0jfznF4qI4SOGj44j/z1Z8wIAV1xxBTZu3IgPfehDCMMQz3/+83HJJZfgkksuwbOf/ewDHsvzn/98XHnllfjWt74FADj99NNx33334fLLL8cll1yCOI5xySWXIAxDbN68GZ/5zGdw00034dprr8Xw8DCSJMHf/d3f4T/+4z/w1Kc+9Umdk9tvvx3PfOYzewqXLV++HNPT07j66qvx0EMPAQBe85rX4AMf+AA+/vGP4/vf/z42bdqEL37xiwCARx55BGNjYygxMa7meSkGh5ooVWuIUvlvYHAlBgZXPqlzsnidXHvttbj00ktx2WWXHZAnp/abrJN2u42PfOQjuPTSS7F161Yce+yx/63m5H3vex/q9Tquuuoq3HrrrQc8llNOOQVXXnklbrjhBgDAueeei+uvvx6XX345PvaxjwGQEA0A/O3f/i3q9Tq+8IUv4LbbbkOapvjQhz6EjRs34pOf/GTP83BQWC7VanXi61//+rKVK1fuMRnxeNhnP/tZvOxlL8PKlUu/UQ7E5ufncfbZL8Hvn3kUAKDebGOKvPjyWnGjf/iVH+535+jv77/6ggsueP0rX/lKAHtO0jxRdvHFF+Piiy9+ws+z2D7xiU9EV1999XuMMX+/p787jvOa008//R8vu+yyfuCJm5MNGzZgxYoVOOmkkx7X4y62m266CR/75Ofw3Ff+JeL6TvisKI6N8JG/+rmPHQjLZahYLO742c9+VgR+O9ZJmqY45ZRTjDFmr4BzYGDggU996lNHPe1pT/utmBMAOPnkk40xJjBakr0HO+ghlz3F3f4n2lxDCgcazRSJJ65Se2J+ycc7VObl8bRDYU6qpQKe9ZRV2LkrwpTUo+CR7fUlH+9QmJPH2/I5yeygbOie59WnpqaWPZHoeW9x8MfbJicnUSz85nopYRhOTk5OPg4j6t2eLIQxPj4eA5jbx1vmD8acHKwNYGpqCsVS+Tc9TDOKIr/dbj8mnPNE25O1TqamphAEQWdf73EcZ35qaupgDcnakzUnMzMz8DwviuM43df7DkrIpVwu/3V/f/8HLrjggsrBXpSPp7XbbXz+85/D6ECKwarMWzvy4FNYrFSRjf66H9x5IK70yaVS6cd/+Zd/WT3ssMOewFH/97CNGzear3zlK9PtdvuZxphH9/Qex3EGyuXyneecc+7a00479X909ezMzAw+9akrcc4f/h8cc/zJaKYPY/uW3QCAoaqEBi6/5KwDusBarfb1I4888mWvf/3rK4dCwdW+LAxDfPGLX2xu27btqkaj8Za9vS8Igj+u1Wqfeetb31rppqIeihaGIb785S83H3300a82Go19IteDsqEDQKFQ+NM1Y0NfSA2QWO0PY/U/UtOl/QFYtT4PgEf1vMATtDO2QmhwgyOSqW5P7AAANGemMp0KPY7VjKDehAN4nvw7KIiWQl//4fKWPhlNe/cW7J4SbYZWwnG5Iip32HAZx65fhkZbzjNb72CuIWDCp/7Mj3/60AHdqI7jPPs1r3jFjbNzc/DLe0dyhoqFaYuqkH2D9nr2/iG5lvFHNqMxTSRj1QhdHQAAIDZAQuZGrL9TjZQuGRBlVaf8/pKE41LdFQ7J9wOozAenGhPTs19tNBrvN8Y8sK9hO46zvFbr2+l5Hhbu5wuv10B1Q1S1z4OrX7rqhmTyi3yVzdALPKslorooqf0sv+8khrHXp2vK6Bh5WB9QlUWqGw4NCwd9oK8PT/+dF8EbEwCTlvvQVxFa3nFjosj3v177uwe6Trxisfjecql8cff3YLr+7zhyHa7VYKECpuMhKLDwzVt4L9h5sveGVa/Jpk5/XjAeYE9/7dZvSWPJE8TUwcnUFVWZkXtAmmbncuS8rVbzXWEYfnR/pGvf91+zavnyb6TGwGNYOUxSu74dLFSB1IMtGxxD2pF8Rsi8RgdyD5/wjP1TNhfb7k2bMM97k6KsaJP145NN43oOQs6Jz++qHVE/JqBqLHWtSsUAIdUfB/v74bouHtj88N+EYfj3xpgnH6Gr/dHpxxkAqFOCthVHUL8q5CYR0aMoGLnIfvioUd5zqE8kLd/4Z8LhPPMPJITz8L9+HgBw/0+vx1xdXPaUG3HBl8XsuDKxsZOi2i96GKPLnw8AOOnZFwAA3FNk8T10zd/hn68TbaBNLRlhpyiLo1SU49Up+PSbbOgAMP3QQwYAho48cq/vSWZEMKqz6Q4AQOVZZwDYT9gnkTF9/f0X4e4brgcAOFwKrlK2AnmdTYA6Uc4cBc8m5pgP4PrwXA++JxtioyViaPV5kWTpdORcfO5iYGAYBc5FH4f5re//9IDnZHhoZNF2sngTA1LepI4nJ6gWayhQxM1RsalUxum68rA0xWEAQG2ohkJRrr1QlevuUAIYvOnC+VlEdT7Um/Jq0oXnDMr9QCBrqdMva/GF58haOmaVnOuB3cKm+E02dLXRZaMGyIStDLokawO5xhJnKKI0tAkqGDr8CABAWal3qYppyYaic5s4xtLeVP7Wd1TQiw8O17UPaWMfIjIGFazzHKAzKwmDye0iMpXybwXqHRmKaTVbza5jy/qant6/dIbaWaedZgCgGkpeYrzZgl+Q7ySAnCtyuXlz8bzsua9He/t9AIAdU/cDALa6Inf7nR/s4pwcuP3zu9+N2zaK2NeWGTnJI5seBAAMHSbieYU+DztnBFgNuvI9bBmX++eMw2QN3kTBuLWrlmHXhPz7ub8rhYAf+cdPH9CQDqr/Ns+N3FWEnBqkVmdZftfR509BFkuEETztKVKh9Wd/JEVER/yefHlTV0mVVoOUojNHT0Jj5XMBAGPHs8CnLDef4YPC6Th4+DZZZGs8+d3tX38NAOCYXeIKH33WK/B6Tzbuq3/yYwDAr6ng1pzjRj5PZcbYIE6yTa9X6+ySTTpZLsjOqz1WfMkblC9cNnJgv5s5gKgtm9rEvRvhleSYy456CgDAp7735BaZh+r8LEKqShZcojkep6WbtesjSeXfEZGFIt9SWT6jutb9RQ/L6AUP+XtNyO/VFqPEFMZupnpO1akPiLC9OAa4kSfcqPwyudI1CWkNjMlDs1gL4DkLFSJdbjguN7DKwDLEbbnhSjNyk89OyCZlN8vWHAquaGxXq1K0xr0JoWGdQyQ3cdrpQ2NG1tCP7/4pAOB/vfZ3e5oX9QZUA92DC1dRKL04U1RFRiLkqIVwTh5IBYsWqWfuqMclsyBesen6twg6Atl8G9fJnqp81aiuayvSE7RndnM+VH3SX/ChjqqYpqm9bxL0Di6PGuJ6LYqiRG37dkyGcm+Ox/IdLB8VSY6jqHJ4x93XoV2Xh/369eKdX/mWD8s4ex4B8OoPvx+3/5EwplrMED3tOFkX92+XNXP84BF45kqh7179C7nnWwQRtzXk4b9mVO7T33nGcbj+338EANiy5f6exvJbIQyVW2655fbbYAcVoSuaqpQEIYRuAo8dhiJ2EfJjVucZecpWV4ziRX8olZprnilUwc1feD0AYNsWuigv+QMAQHH1y1F9KotnFklAdT95lx8jT263LU/KlUdI95t7b5auI0e2Gzj89yQc82pqk1//Q3li3rld4vXFsrhNYatjr0Fdxl6stkZQmlfbV+XagR83nhWI8JX3S+HC+LbN6D9C+PFDxwhCiOlyl6mhnbYbKBOlaQeeWkXQbRTJLxqNedvxRvXPa30SQvCY6PYJDzzfRRwy/sw4dC+mKDnVOLdJkToap1X3XJBokfr5TjgHMNYdFOW7KQ8cAQCojoqwVVCR40VOYo+jGD1lfF3DCI7vwy/L9fXTW3MchqjGt8lx2i3ELZlDrylhsekd0vFoZypoa3yHuPbzs9tQZVim2r+i5zkBAFdzAI6GOGA9XMfV0AjRNrsK+WmCuCFjS/oECXqcB6V5Z1pSns0X6LwYnlNDXEiR5ST0vUY7hsmP4ewk2k16McSMHsN7IdFzSC/PcT14it6XsFaiRI43TK/sKavH8B+z4lGtYRy6uZveyoDMyXOG12NySDyEOzrivaz93Rf1fO7MAnz4smsAAJe+RYTLfvSIHPfkYyVk8sOf34bX/Z5sSietkPV597SM4aynind35zb5+ab/ugulmqxvL+xtT8kRem655ZbbIWIHFaG7ZSaiSoKCiqUSwN6cCVkCHpMbg6MytJe//lgcc4x0ytnxNZHMHWd3kDPOf68c+ATqRrQLwAHIIVSeQepkQ6RSVz39YgDA6OEiM3v7NV/BMalUgh59xnkAgCkWEm35oXQVacuPSNwiUgEj8P3en4/3/VISlievUr2Y34zj/ovvi5Ldtl/dCQBIi2U0asLm2cSuLGFTUI3DXqzFYhlBXeK7pVi+D4foLeTPcQoEgXoRRPhstlCmlHCJidCyaxAQtSV7L2rbrzldMVVF7Ymyn5i8LRCtRnEEh/H/oEgRNpddbGYlnhvNy/iGh6vo8HqnGNcu9knMs69f5irwIsTsTFSvy5ftE63WBuS989FWuOw0FU0Kah/f+HMAwOxGGfvWpnhMw4cfjuGUMX131ZLmI+K9oloynmOQaIKSU9UJZayBInTPBTjGDjvvBEXJKTiOsj/I1DFpFjs3emBBt0ZP5HpZokCZKiReOFwrjdnd0ByzS5plwiR9rIllxs2NSRYwXnq13RX5zsOGHO+G+ToiMtSOGFsPABhYyb7F83Kjbg9cbBwXKeNzXnsBj/QbUmQPE8LGKxktuP7TVwIAbt4upK6jRyt4cJJ5FY33e/J9zEI8yFWB5LR+hWmc+TTpnXv9r/IYem655Zbbb6UdVIQ+o31CyWBx2olFYU4kqDHg0/75vyOx5Vc+/WnYcv0XAAD9j0pjgBP//K/kgCeQ9dEt+aCAUCGLv48nb0C5zoJkwYPjJG5+zFMfxMwdElefGxRmSN8xEoceuEviX+NMZ8dxjBJjrIW09yz96Ohy/us3/CqIhB++SxBBY17ipoXD12AXEfmmG6XvqBsJ6lpbFYS9slhEQJcjndNencp84OhcB5WaIF8NoZbokdDhQl8gc130AJ/z7++bNrtns71Ks58tt5hT7BF9a8/X1ABeIOML+gTx1NuCeGZ2ybX1jYrXNT+3C5PbpbbJTcSla84rX1osSucwsVO6u3uq1eMLwh7pl9dSrYSIn0u4HmbYvKJvRBg1y0cFaa0cCxEYQYizU63e5wSAhpo9slSSOILLvI1ywxOONSbiLBaKcDtyvrAujJtWScZfZg7E5hNcB0ms/WIVNSuDi7FwAxjeZJY6yu86JM2z0w4tAve4RlpE75ahowN2XDvpe1Ox3Jft3ijx8tIq+c69+R3o65c5uS+UfNeZJcmD7ZiWE7189VEYG5VznX/e41s1fPT5Uvfzhh9fDQD4p1/JfbhixXLcuUO8gmePCrNmhl5/0pB5u2OXeHnPPuE5WJ7Kd7WmxynJEXpuueWW2yFiBxWhx0ThszGrxqIIJfJby6yW8mNBTOuWvwQAUNs2hpl7W3yPVIgWjj0PAJCmwhOeuP+HAIDJbcOoLJe4+NBKOU7/qLzOM+Y9PjWPQRY3oC7jGRwShO6V5fh9x7wBW+/4FACg88i/AACe9tSnAwCOXybx0/EHJC67Za6OWlXYA07Ye7z4nofkqX3ESczwe0uLof/T3wuP9tc/FTZOsU+uqbJ6Dea1ZmZKUKRW1ZVGGcsrl9BknLXaktfBgLFPX1D8rBfBY8WhY4tZ5LiKADUO2XGNLRFdygLTWKo6PA4c6xa4hKnacT5NGOOFiyIbJFSG5PtAU/6WtiRW6aTUqW4B8MTTWrVC6hXmdsv32enImmokDfgVQfwjVZmn9pz8TXvIDpb7kDRZvETCtuYeUjK5hsmTXlatoUrmUHv77iXMCuA4rFEwWgfgwFGXRYt80oXsotgrINB4NVFya17Qn1eS+yAo+PYaolTnk7kKW1VMZk2a2Ji7IvOUsf0m58cYBw5zHIrm7TAX1LdK9a0tflxCGHvdWmFutUOZ01PXrEXiyzWc7Mp6+O494m2vrsgJvnnnjRg9WdgnxdF9y278YsMNOPFlvTBg5Ht/w5WSG/v12/8MADA03kRzWObtbt4vEefm3p0y9n5Wi298ZAdWHiVruHAAOuzddlA39CrduTpvNM91kdKlqDGxtmJQklIj64TaFYzvRDAri3PsuUJJdHx5T0wXZisTgMcc+/tw19LtDtT5YJKoJAR/vw8olyUpFUSyAO+8WbScn3XKWfKe49dh8Cnynt3b7pHxdU4GAJx4hBQQPPRT0ci+L8puZkS9b+g//HfVTH6F/GLg8J4+/2+flyrZu38gla0gFXHFOtGWrqxeiZlp2aRLHntZlvgQHR4BAAT9NRSYJKzUZbHVtHqQN6ZTriBW15ivSSTnciA3dEElGhygwxroemcp4QUWtyh90RjRbADgOfrA46avFaReAL8iN4RfkuRt2ZGHWsuf51HluuPEAfigcpjodbW4RasW0xQOE4F9RdmIE1eSpCGT+34Z8GbkIRmxsCfiJKWsIF2xWtZRxU9RYNHY2iPaS5gTwHCz1iIdk0ZINBSl8gRafq+l8FEEhwlSo1RBhoeStlyP78s9YzzXzjkWSWZoCDNOAddVuiIT5015YEZs6ADHg8dqzSRlAn6RzEDStYerJIFJe79/ZnZIiHGwJhtgY2AITT7kH2mwEIxjWDe6BgAwn6Z46//eq0yMGJO4H/riV/DOZ8vmf8rIgfcMLg7Iw+S9H7kEAPDON7wBD+yW6xvmDXTEco45kJDQcbyP7mvO4Lt3TwAARpb1BvDykEtuueWW2yFiBxWha8Im6mTaEZFDKh1dvSFSjIpD8hSr/+IegEUq5VdLib5WQpghSYS0HXEdq099KhDziWbpiyxBbkvR0IrqKhQCJiIPE4QysIvUoIFT5PAD61F4nngDuy+VcM7WBwSNjg5L6GV5n5Rv98+G0OdiO+69MOLmByRxd/213wYAvORP/gzAvttjzezahZ/9ROhxN39PvIuU1LrakKCt8uGC9EujK4B5QeYuw1qKPAt08QbHVsCfF9TWGBdUU+Lx2iya6qsU0OC/UyZVC6TxuRF/r0VhnZalF8amdz9ahbIUNUt2VBE6QwsqoqW6JG6AgDRKh5IOnqeokMjUsY4+jBXs0pcCj8djmKaNAPiqNKbhFBYzeUEAn3RbzeCqpplHjfz1q4XO5nbaGJ+RROzO8fsOfDK6TEv1EyLQ1KRwNZSh87GISmjSBKrQ6LP9Gvj9tSgJEFAiwe1qXJ7aeFcmuCU/GltYZmJZIy2uHSuIVyjCSQRtdiKeUgugsquR/3sOXCvD0PtaaUNOsKUhYaSjvD60GV/93TUSQh1iIVWnLuGvUjnCcc96+r4PzO8YqYdrPvMPAIBT3nthz+OrLRNP+eUnrcR9122WQ4/IOPp2yLzVGS5qFeR+nG9PYlmfrJ96vbc9JUfoueWWW26HiB1UhF5vUYiLsTzPAWLGaFsdebY0GUv3qvLE7TM7UGIM0NksimhYLvQzTGh8VmJRyfYJRCxDLxXYHgYS645nJSbl+wPAMo3VyZN89VMkJo+ECoNxG2DP0VEm2FYuk3iz0sSGanzKeg6ailQLvSP0mUlBSXfeLMnMl7zp/L0mh5pNGfenv/glbLv5v+R6pkhPJH2vvOJoGdfhEi/0qzVwilFWGVUtDyfNsDI8iGRWrrM6LF5Pp7kZANBiLDGJYngDkqBpUx/ATwTNukTAIcWQothYGeMo7H1OVArWirk6jqXlqdKfw6SfSiGncC0lLitI0rgyr7cbbToL3+vYz6qqo2Ope0ZlKpnEsgqlWc28Rf8WISeytiqBrNHdWx/A+PZNAIA7H7j9gOei21TSOFM/duDTi9FEskmUdujYcUXM7Sjd0XUEPaeMobdJNyz1D8Axev3qAfGcHINrUjt3bVJjwzYLnjgWPwjQacu9pN6Wr3F8613oIs/m0HpNPdg5J0tT6U4oaPeObbM45XBB5hVPkC9D/FizXjyRFg4gFs7vfG73g5jbesQe36JKsY+V03usnf2Bb2PDT58FAHiADavOfJaIDd64WfamdlPWR1ytoP8w8bB3PbDxAI6eWY7Qc8stt9wOETu4MfQ26XKU8UyTGB5jVSHpPqYiT0+3JJlp48xZ5oFZLcU9CtcKayQ++Ry+Iga8tiD7dkvYKTDChPEVlZYHARXRchjX81lU4qvedQkx2+WNzwoTZmpaUMzI2BivhgUtiNGiLLBZQum/ioh/73YZ7+/+9Ac48RnCpKkMCF3uwQcl5vrd714HANj8y5vgsSw9bbJkf0zolINrZNzBkPycwIU+tzWGWlRKIZkYqe+jOCafKx0maKE4KR5OmSjMdVOYCgXJGIPlZcNlHNspkVUSpkgoMesuofRf5V2zhhKZ06ISqz5plTFZNMbEcBSdqo4Uz20FvbR63XQ3cljYEMKie2Os4JUVqlIKnhbWJCG0X28WY+Y46dn84sG7AABhfQL1WFBkUOzvYTYyU5SbMj7tOL6NlSepFvuIaT2KgWOvKbEImk03qP3enpPv2CvVUAgyWQE53kJ30cCDIbupXZfrscwYfidR1LaSuh7vNWOplgtlij3XtSh+KYVFv9ws638Nu36NHubhFw+LV76cBWalglzn5vul0Ogfv331ARxZvr9+N8bWhznv/MtdG0T7/K/+jkyZ4jB++MPvAdhXHaODf/iXbwAAzv+T/wMA+LdbbgYANBKZkxcfI9cQbH4UD2/8NQAg2U8+bbHlCD233HLL7RCxg4rQ+4uMjdpsvWerU4wjMdx6S+LZrY7EydveEehAUCMmJK6NlRIjsyXiBIGOD/uIfPQWQdSIBEWsWk/ueseBWyGLwmNMbVZe+0bYlSFNkbLAxmdHmxr58Y0JicuFjVm+NUIUMY4b9jojmdzpdpaD//2H/h6HkbtcZGx+nhxxDeX6zQgex1cgIhhcJTG3wmFSKKNdWxLENuiqza01BqtozPgeSoMy//1j4hU0dsj1lshrdxKDtpXPZaMFcrkTRVtEjklrHp52vAl6xwwqGmXxsTE2Dq6oWy9CW5qlcYSIEgcp4aExGl/XA7v29ypC1SZfvq1iUsrrTrImDzG55b4rbwoCFu20m+iwqUaqXiNRqu/K3Owcp6SFN4ddU+ymZdb0OiUct7x6vI7YxJbPrcwT24dVSSoubD7DSchyIhsIjGcnHfH2okYdAfnTljWubBnLbzdoszBJJZINUbjG6FvteTsuXxlptjMav1uVKzZJV2vDqKf5AIBbtgiS3d6Rud09O4tzjvsdAEAllnG+bJ14n5/bKN7SyIoD6UEq3qg7ehSOaQlL7Mzn/j4AoOjJvdruCHoOkg7Gd8i6XLly716GNyJ72hnHylxs3Cyvp66lJ0453RKAnWzdVw16y0Ed1A19iMpoU2zrFpoUNbp2RbqDpTqTPE3ZUMLDT8R8kcqB4cIv3LRJbZyiVsdwYFuC1VbKlwpfwjGdw2TRTAajGOPCq83LeO77oSRbTz2Tk9fXgrtNChZSXxZ7pyRf4sOPCM1wF3fvWq0Ct8PF2e59Qepdqhv7/Q8+jG07xDUc5M3lkc40SnqZX5+DYcilvFIWQ3mFPARqy2Te9KHppa5NnNmQCzsYFRgiClIDX7XqRyT5O7hCCrsak/IAS+I2CuzDWOHDYpabVYuFKj7nJHACFLkpq9ZKL6ZsQ5uUNMbqoWtlqEN6YUDaoInbSJoyJxqGUU6f3eRcpcoFSFW/nAqETklu4ALDEZ4JEFFro0VKXMINzJ1hyMlpItUuQhp24jmiJtvzzcm87px8FLsnZJ6qfYM9zwmQhXq0PZwxzmPCFRolSmJVSTRWeTHl5zqs1NbPGD6I240ZFAlgfH9xcpQbe9xCa54JT6WScs6MrTJ1s8pS1Ti3BUuqsqjFY7AlwekSwnM1LZJj0/YjOwEeJPA7cbnM813zsgbf9oFrej7+OX6Mz8/IQ0PFIJv9su7/9X9LMeBrrvo+5tiOcSUJGns2GeuffkwIED/+5YkAgOPWiF7U2KMyr9/b+ijiWObpyPV7b0259zPklltuueX2P94OKnS9KWgAACAASURBVEJXpyth5xe36CMimvKMPFWbk/JU3b5R0NGJp74YhTskGbjjX0QfYX0/VRbX0lWjzndzagbFPnmy1WriZnlVQQ8xJQFjBGi15bLbdHnGhl4qx4ukKCe69ZdI75Veok9/vhQhuUOCmu956CcAgIfbgvAiN4DDUl5nX8qOezF18W0NjEnRZDVGhYhwjE2tS0Q7hU6IgFzEQdKbBojQA+2kFCvlDigw1DLIYgXDpHSZCeg4joECwwmkaXqkafbzNdq6FWaCDXRZLl8kIksYAohcLcpxbDgncJcQcmGiXHuYGnQlRRk+SAxRIdEnwhZCouL2nCDq8hCTvgwxVAbkestOgE6bapnsoFMqCDId6pdkcieMsash75mYke++VpC5GNKG2pMbEWvlTJFzyeut+IJ6t20V77LZqKBUEa9n+diBuPyPNbPIm/Nc12qk2JAGQyTa0DuByRKmDEFR7BQJ0bIi9bRTR5NzVx1etuAz2i+0PjOJ2DYRJcWYpfudsJsmarmVHP3CBLNjKaQmS+QuQcfo6HWy/h1+95OFCl7E7l8bpyRUmw7Ke95x2rE9H/8PPvN/8c3XSt+Ftz37HADAme9/FwDAhRSK+V+6AdeFsh8cu0+EvtDe/9mrAAB/8aY3AABOWC3r45j1x2L2QSl23DIx0dN4c4SeW2655XaI2EFF6E3G7kJNgriOjZt5KalQ80Ku/9nPvwkAeNaz34zaCqogPiBPq/HviQLi8leKImPfCok1x6ELpyDHqwYSd3ZYTKMoYC2YPAUQD5HqdKR0Nkl+dS8AYPcNGzAUCooq1kTN7Rd3yhPzpvuEQrh1Xs4TpQkcIlO/ciAlBotMCzms/nRW+BNRHc9l7kB1zJNWE7WVMvaCJkNHBFFlyTEtmIkwNip/WzZMESaioz4izcD34amQPJN6Q1RibFOvPZichsPYaY3zF1BsatesxK7nGC4PPAeeTYL1bgZafJIp89kYLjGI6rWXqBToBS4M1TznJ8Xbc3xJEI8dLsmogLFeoIhVawStGcoVuEz0FquCEsvlEVSZs9Bkq9MRxN6py/HDVgyXCogu+486LLOfpDdTDBIebwgrVkg8dP3akaVMizXfinMlVn3S9vXsol0CEgPX9/hK+aRH1dH6qK5YuhYL+UXx5goVmY+YRUhhcx4JPxcwJxMxBq+FT0lq4Gl/WUX/XMs2ho5sfFl3pN6183c25LvZ9oiszZe84Lm48de/AgA8ZZXM99pVS9/masOn4zs3/GQvf5X1FXgO7mcXtV5s3VESQ7/2HX8MAPizT4g8yTa3g/5lcn8/bVlv3lyO0HPLLbfcDhE7qAi9HSkkkBfHdVHtH+BAJP4232Es3ZX+nhPuKRh71gsBAI2tQsxvbPkqAGD5TtE+N01B0YVj+jLh5Ux1CUBWaOHJyQAARXbfNjcJMmje8TMAwPTE3TDLXwAACPE8AMDG+0WmtglhkbRBqVDXQYna16vX9N7NXQsstF9nHEdwiV4qRFZBS1CIR8kE33fRR4ReWy7nTB3VqKYeNpF6I0zw4CZh5mx+SLwfFa064RjJrh99/PFIOCmsrUJtVIoy+kcEoc8N7ECLLJKIuu+JoXY657NiCNFNRgdUGd1ezJbwO1lOwhYZ8Wftr9lyZd4qpRpMKuNLGB9v7B7nZ9jDlvkB43kIPCJreiSq8a4FMJ7nwSECVTbQPGUWmjOMzaYuXOYsPL4mWujTlLmok+o4PFzASEnOVZ9SWYreLFGBK1v779pClowfwri4yt3CsTF3vTX8moy1SWaGehWe4yImDbNDWq7PvEHc0A5dsK2TFIV3Yq0w42BS0xUZ56/46thir4x26rpKU+0dX544LLmjair7yPSuh7CLwnKlabmGt/3953s+bi+2+tjjYR66Q3543u/0/Pniq94EAJh+9xUAAFMo49TDpNPV6895aU/HyhF6brnlltshYge3YxFhRJvBOzftoOMLIirVJAY50i+x241bBMVcseE/8L7X/S0AYPX/b+/N4+yqynThZ+3hzKfmSmUkc4AkyBxAg4IoCoKNSAPyOaBNX227tR3a9jbdXOFTGofWVq/aoq00TiiNikZRBJRBZEqYCVMSMlcqNdeZ97TuH+/7rl1VYahTYKHV+/394OScOmcPa6+99vNOz9Ogp9W919MTN/z9TwAAvQVCkQvdg4CFXOObIpIqqPaJB6Ej0/KPe++jff2U2naXSQdz4UjMO5XiW0/ZpPO3Z/9W3gDXHXMgUdkOMlLhUGu+Dl1xG77Evh3LQporRPJcU58WZQlWyiketAAFpibIthKCkjCkF06MXfv1OoYGaCz37qYGiSILQFQWELoP6jWkWcVJYvk+I/5sD3sCfd1oDFPNtlfmGnghxhLFC4/b/ZWDVEYGs/m8gmk6kYoI6AOajYyAA3sLTqqATFaqnghJN1jgwGPiJr/KCk2tc8x8U7a0zHM9NyPsRnUMpTH6fYNf62WhGWAqhWwBmRa69opRvMXX0WfismyW5p/r+OjvJ6IlP2reawEAZYlaU0wdHIkjagjDhHKYP4eGEoTOfxMxDhlfg+BdFzoUhE4xaaEAaHBNfoTYm/SkMSmU/M94VM5NWVyZJYdnSMwMZYEFLXPWmjh3p2K/uo+Q8UErucpr7xhO76bja6xYAgDo6pnf9Habsau++UU8J6PelIzyYJ/7/D8BAG66+hbcu5MoS/7rVqLVPv7DF05pSy8LH7oNljKLRmBZdEONspuU1eQOZlJ0gbbf8CB+WfsBAOAd/4vknI5mN/n+m64BACzqJDFf3f8gdIpCBaqDkwkWl7cZPo8I0UPExbDzHhqsoEDJ0Kc0iUSvftNH0D/nNwCA3377owCAzXuZ89mnG7TIzTm1yEaFWSQj7kptxqSb0bZFPNdGF0upFXnyZ9ildbLcfDVvHjJz6CEW8kJnGbd6Im9JJu3goHncfAQKseRYnm7hQTSR0m7aiDqL2ywSb24LXY9c9xyUeykcZlW4cYdDQA0W/65zwiuo1uGYJG/zi5csIkEoC1HM022Zbk9ZGLjTMyhDcwIum6WHulXjBhguTazsp4dzfXgQtigVcQmnxe8j6TZt1BCGLADN5ZkiAWdnWBkpn0bI4CAlbcJcaqlYEFpFtN2w4WGMz6tSmY6KUxyC0vHqGBcDCkeKMEIKAyi0GaOIOUMsSQ7X6J7zOcyiUhlY8pDmRGeFG6+MjrTlmNiNsKaOO0I+LGcc97wc6kT+eduSc4ni4EzUfFL0LUdSwcRtj9O1/esP/iV+yVoBrz75/U1vb3r2Yhbz2Faf8T4AwM9+fh3m0ungDw8/2NQ2kpBLYoklltgssRlF6G6ak2k1dtlTDlxGSNLODE3IIBfQs6bTL+P3N1DLbiqgBo+//F9vAwAc1fV2AEDv01sAAJXde9B34220r4BCJFFICLPRIBTS1j4fA2OEHg5+5asBAOWl9OosIeS6p3AzrvuvbwEA7niAfmdZlFDLMkpqKxJKa1Q81LmkqzZaaXpMJHQgPC2ZTBoFbmfPBLRvn5ORdpYQ4vD2XfAGWfCZ6RQM4uRXV5p9HG1K0BYUKHFkczNOMEgIdu/+R5EuMgMgc6RbXO4pJXKBcqE5LCOTpuZxeIJfPfY2RseqsCRcYjePXkSYWAkbJuJEoKmGNGiVvaMogsfMdIob11LMDgnWuox8Tnx6VQQeXSstl8yEKGKebm0LVQCXpXLzkcvXoVwbgxfStUmZ0JIkpXlsKqxG4zumXNAPmkei48/VCC1HgAT51CSNVbmPoOIQlpQO+sLJg4lhLM8P4HKSWbGnEUXCjkjfsRwXIYdsonGlkRM2pCODyI1GVGT+xecix6kNWh+fBJ+qbewjj2oxK3R9+8d3osYKRZ9889qmt/fyGnmYf3HCu/BASEUhD3Loa6qWIPTEEksssVliM4rQ05z0aDAsGhnxkcoT6ityt0qHHBE/0If8OiyLcMgNvyGkXq5Sc8+rz3ovAKBn7XoAQKEYwZu3BgCwdQclAANfVF4IedTSPThk5SsBAM5cQuR9D1ITyP0PUkx+x9PXYfPDhBbaU9SUkrIY8WhCBGMcfwxDbdCIJNqaMomBMxJuzWfhMJJSnEWOqoSo6pxnsAaGURUWPEmqCoMlt2NL6aNWgFSLuiaBZnZOn1vjCsYkhs5vbYgie2Qmi8dIURgoRXXKSnMTV2srPGZpnI6Su8XnoJRvDjMyDVPMGDgpRqu1Rshx3yrH7SOmPEhl6Lo4KUlcBgil0UVi/ONjxAAsJw2bywwDThRKwrjK6N7zDTY1TJQS41cmFs1UBbUQthCmTYc3H+PizqEwT2qzv5hn3DJ/k88tnmPCp15nD1lKZqVMNAw9OKJqJeMseqwyr2zA92XOWhPOMTJpGMvowYr+KSblPgwWV5ZJ1o4vvpyq2ZqI9V57JJU2/+GB3SgeenzT2/lTsMd2/RQA8Jmffxebn6JoRKlUbWobCUJPLLHEEpslNqMIHVo4kBnR2RagRIWFNUXL3LDDz5rA0Shk6ale5Pjw7++jcsOHniBynCN6KNN91CuORnUJteP2rF0CAJjL1SgD+wkpjNYCbBr4AwAgdyc12lxzL5U+7bCpfdffX0WHQyV9NjewCNIMmL6g7sVlV/KZq5pXXImMhiOjJdiIuNFCM3JxcxzXDGJNy3qaPsu3UUVH1E7x8QJ7CYLYte/FiutS7aBFsYkJuSwLthaOeqaIlTK1YYrhWWNjaHBVg6DjiBueUnwMZfaGIuWgzt+dDmKQsTZRV63HkTwJwoviv4H0LE3lC/++3uA4NlcQORwLd1MOnCw3bcWuCW03Eg700ORd6oEgdI59C7GUbZuYuyWKR2ri9XQY8UdRZBCsrZuPFQNAGE7kJrdsy7TLR4b7Pa6AATjubuLZXDXFl7/mC08x/wRM1AaYriEttMCOqBF5sbc0OR4+joArnFQ+GytBTaLl1bGH60xDsajQRpVbNz7KClvpVlz5qf+/6e281Hbrj38IANh4K601T++fgx0lqpw7jEnMNvaylit7tkvm0eu2fg8FFkazUvmm9psg9MQSSyyxWWIz21jEyM7J0pMok0ojl2MSoBSjPp9qdBtVes1aGilGlrk0Pa0KWXp81Ti2vGkbNWxs2tWHWhsh1ROOJWKe9R2EWJ95gmJtD+7twzDrg+Y5qDzCsdoxJsLvCjuR4kqEWkAIlQE5PAYhQpOacVPwBL3XxLuYuvkeExsxot3j92Mom+Ux4eaUeaRw08X0ry3FAhyOWztcU97FwhZzemhsLG5bR+Qh4Of2GBMZDY+xaAe35XcWMuhopd9XucW7to8QT7mPiahKVWSkLhyioUpegsPILFemhp5wpASb6RAqfvMVHT57CXFNvTVOX3SS4AK/BlrBdqT2GnycUgvPDTDjEGWDY+fCvluvc8s/RIAhNLqYcdxeNsyNcbY2+5d4tKgaGc+LK0yUsoz+Z92bXmOR9AYYDisdIa79nkh1ocbDbvFi+DsRN0ZBS4NRbOKhSWWMy3PQ6JJGGq7DlNQB19rLgJsNjatDn8TEoS0RfOVzUtp8SagNmrF5TB722OPUi1Joc6Dc5pH+ZHvmCcrTjd1fxeEXPHs7/14WUOtybsJvvkXNid//AcXBd4D6RCou0Q+EYxV0t9N9vSVgoRmu4jrpCLq/gxR5uqefOhdfuI6ows+cO6kx8gUsQeiJJZZYYrPEZhShRxHTkHLFCVSEiAmdpJ1a2n9TLr125/LQ/OQeG2IdTY6LS8Odx1AqtEbNvurc4NbO9am9aYq3azhoKIq5l2pUIzy/i1CJW6N9eqFGwDFERwslKNdac0zd5tb29rYMSuCqnXJzGWkehHH/B6qVCoYGCelK23SBW/XH5lAt/Lz5803czZKuvRamCzahZv5cp9C3n87zwYeIVvSpzYQ+sjyOK1eswIpViwEA+Tx5NLtLdE5P99Kx9O/rjTs3pTqGY542d0m2cBt9R8pCkT2v9DSqXKSWWo8bmwOitDqOwQIkr6bZa5J6b5GDM5UgkKqM+Jg8T+LSXJVh2GcVYMvv+bNQKofijk2pj7eMlqfEpZkCQHIJloIgUdle8ybjIhDdMrFo6a415d5yjFGEUKTiDAnXs3tNCnGeIDDCFgL54xyBH9Qn/M1QB0gXqB2PoxT0+KL7G5e3mA8EzU+j8x/Dg3QshUVM61Dbj/97GQlQvOVt1Fnu+uTZ3/Y72sF1//2XsJjq+olnCGb7daY86KDP77qKvP55r96BJ9/xeto2k6qxVo4R9tg/N43TPk7I/IyPfwEAsO131Mb//g9TZd7jNcBStGB5edrHkUuogu73j5Hc5RNDtH7ZN9pY/ypC7bc+QwN3yRTHY0YXdEuIyEVkOAwAbgJpsPKLxW3ubcxnnosclJhDw2P3vcqNIg1+CGT5hs1FERQvqgXWG7Vz5LL0M+fHnrFBhNK4wqyDi3PUMBIN034ego9RTjrOYSWfDJelBRxzqdRpESsNlBBp5lzJNV+2KIu2I86SVjGbHidbJQlVZ83SarkMmxuybFfCAcw7zTcFR7AwUi5jyxbihXjmSUoCD/YRW6DsxrZsuEynMGeuJBSZ7501QXWk4ToTF0bhJPdl33xddQgUefFoyTTvBMovTGBCqbgxZRJntlbjFn1eWMJJjJsyNu64Mrt4URb3fJKSjmXFvzfc3ZNNH/Cv+GHE52KaZuJy0WgaDzkgfoiJtmgE2yyQUjooIR6TPFZ23HwlCVQ5NilxlMU6ilkQTYhEHoZG71PHuqWGmsEa/xP4vg+Hr7/hPJrEOSOi2vSck/DUVEciticDQm5f+/+ItuNrv6rhkY13AAB+cj0tsksPor/17r4LAHDw4iLu3UIhGpcvStsqClXe/pt7AAD73n0UACC1t4GVEr3iB7w0c8kDyN5Tgf57KpvcnSFupWU/ogbHn/76AgDAX735L/DgAM21sRFaS0Zz1N9f4ftm7WJaxMcG9mH3Fnp4nLLglU2NRxJySSyxxBKbJTazZYsWoe6QlXcQhvC4Wcblx37E4ZORKvOjwx8nGM4EStbEkrAal1oVOrowNMJhF0b4xbzwrVMiYmxkH+o2hSDcFkLvDZcUfdKijm4HqLGn0OCEWhttDi0cZqjW6VlY81LQihKT+WlIRQrytFjo0VHjSrpEC5Kxj98gr6BRq6HIjTHS+JMW5R4eK4/Rw+DQKAb2U+PUsGgsCtMdewA7t+806LXKNAMOJ1UFVVqOA8dhDnHeicuJRY+PS/E2VCYDkYcM0DwDZcANQlLOpqBN4s1IZ+oY+fInBonGbeVSDifJQGmSUaYpRpp01CR4qFS87XhfgtjHeQWTUGXM803vBY3Hre9xE1mzJgyFwmGlEcK2RAFoYkORUDYoFTs1YUSfpVy6JpLgF754WKFx8dR4BA1AlIYQ+iY8apSOTBOTnF88eFJGaSgVpEFJi/9ljZvvzSfQT1x+GADg9l3bAQA7x0poyRJKfsUKCiPesfl2AEAHj1V9NI8CH092LZ3Lhm8TYV/tzGMBAD3smZZthfTrT6IjfRORZ6WWE5L2d1EYN9q7DeWv/AMAYEGF7rH+s6jBsfun5BX84Iaf4g2veSsAYOXaVwEAHnnyAQDA0hVERNjmU+HGwa9ahx1baSyOXpCULSaWWGKJ/Y80ZZ6uiSWWWGKJ/VlbgtATSyyxxGaJJQt6YoklltgssWRBTyyxxBKbJZYs6Iklllhis8SSBT2xxBJLbJZYsqAnllhiic0SSxb0xBJLLLFZYsmCnlhiiSU2SyxZ0BNLLLHEZoklC3piiSWW2CyxZEFPLLHEEpsllizoiSWWWGKzxGaUPnfduiM0ADhGuchFhRVjikVWR68SbWUYEr1lS0crfP53wSZi+I++40MAgFamzmw4WwAAx69ZD91KVJT9Y0RbetPNXwcA/OB7PwcAHPuKFTjxRKLB/MUdpMydZ42/1i7SIdXKR5q5cNOsY6pYOT5iCl9XaFddF0aJhjlT3/3W106Zqn/B3G4NAJGozisViwsYNZ6JCvdE58p0pvxdlwUvjO6kUMUiMtSlQrEqVLRRSDSqQaNhRCDstGyHvytq9pE2OxMxktDQuIooQkwzK6IQPtMQDw8PTXlMTjvpOA3EijBKhUaZSc7PYUGOuR2kSLUsU0Bp+1N0fD7Nk6PXk9LMsuNPAgAsOIzmS/fC+c8tG2QUh6qIWF9VMyXztt/dDAC45drvAACiUh3FBSsBAP1MJbtnjAQX9lfoGGoBHXeglFG7cvha/fy3f2hK0mHl6uM1AKRdmtv9O59ChcVV2rqIKrZQJFUroQx2lTLCJDWhF2ZN04G9DwEAqiUSdolgQXiPnQJRQh/UMw8AUCrTb9L5HNIW7bMxRmo/vft30u8dUqnqmH8oMln6Tit9hJCVm5AmHmqbqa+zbg5+gxW6mKr63tt+PeVx8fjmE51W27WNelSavxMZgRKi6o727sQTDxB17QP330/73LQRAHDHdhK+OHrNawAAx2er6H6YxCq6WPkoELbhQw4FABSOPQk/fOgJAMAWFkxZuHARAGDxHJofJ65bhxOOO2iqp4VYToTMifVDntcShJ5YYoklNktsxuhzlVJv6u7p+oVSChlb9ENtgz6FmN/36CktCMOyHYCf5il+bukioecTlq6m37STvmY+DI3K/P2Pk9xabYRI421FyEMjQFs7KXK3ttETd3iozN9hkQjbQTpLSCKVYdUK0ZtieCtPwkiHsIT8X1lIZ7K46Zc/PlZrvXEKY7K6taX4mGXbsWamUkZGTk/SzRSBA3o7SXQh1via8N6yLXN8YSD6liyGIAIaUWi2Z5C5SKfJaavYU4hEW5OPJQgCZDPZCWoPRhqNT2ZwaGBKCEMp1brsoHkjYTT+swgpFgBJOSxyYlTj6ZrlnBRcFtPwWKRDRCVaWgm1tvJ1z3V2wS1kJozTODUL2n6jgbA8DACoDJNU2EAfyYKFAY1Rys0iZFGUCuuUNtjrCcMIC7ra0NZKAiuRsgz6F4/px7f8fqpjogD8VVv3gm8qAA7fB7VqGZpFR9xUjrdNx59mT8tGrH1nxCb4mjaqdH4ee1zROPVWkZVzUrT9VIruA8e2oDSLv9RonA065mPI5HKxwIYe581pIN/Sje6exdB8rSwEqFbJAwoV7evhu38z1XE5+dz3vf+3jWoVOZ6vKcvIdTyPaYR1OvbyAI3BwBCJ3uzpp2tcDej427rbMJePz2F5y7RLx1mYQ/Npb6OGPp4TFovm9BRpbZlTJPGcXGvmADGUqVgE8r6/+fWvvxPA9/QLLNgzEnJxXfcdLS0tV779gncgywvlH9tWHXzojOxnsg0NDeGOTOZWpdTrtNZ3P9f3lFKrM5nMXWe/9RzMnTs3Xlj+zCwMQ9x4443YumUrXDf1os5DKZXL5XJ3LFlxKNatW2ceLi+/HdP0L0ZGRvCdq6/GUauWoLu99UXtPZfLfbmnp+c955xzDlxWkvpzNM/zcM01P0SvX8fchcte1LYsyzojn8//aG7KQaFj/vQ3tIReDn4Rx7L0Rfx2KuZ5HhYsWPD1wcHBVXgBvegZWdCz2eyln/vc57JHH330TOzuZbd8Pp//xje+8WEA5z3Xd1zXveiCCy5oef/73z+DR/bHsbPOOgtveMMboKNwnCwZ/c1ubn1fP2/evCWf+cxn/mwfcOOtra0N13z3anR1tMFxbCPKjGDqUmtKKde27fd961vfclpamhch/1OzE088Eeeddz6KHYRuI1033kC+2DXl7RSLxf/zL//yL7nXvva1f5Tj/FOzk08+OXfWWWd9TCn1f54Ppc/Igh6GYevcuXOf9zta6+e8iTds2IC2tjaceOKJAIDt27fj6quvBgAsXrwYF154IS699FLYtg3btvEP//APGBgYwIYNG9DZ2YmnnnoK99xzD4477jgcccQROP300wFQCKEZFPiFL3wB9XodpVIJl1xyCR555BH853/+J5YuXYrTTjsNRx55JABg7ty5sG17zvNtK5PJ9LzQmDQ7LmL/+q//ilwuhw996EO48sorceqpp+Kaa65Bf38/SqUSli1bhne+851YuHDhC+7jueyTn/wk2tra8IEPfACu66KtrQ1jo6Oc1I2TojpsSuG+bSreSjNjUq/XcfnllyOfz6OrqwsXXXQRNmzYgHnz5uH222/H0NAQtm3bhrVr1+LNb34z1q5d+4L7mGxRFOGKK66AUsqMO0DzIAhDaERw3DisplVT2pkFx3GiF1rMmxmTwcFBXHnllQCAO++8Ez/60Y/wu9/97iUdE8/z8G//9m8AgHvuuQdf/epXsXDhQsydOxf1et1k+1qLHSYGFERNhX/be3p6nvcLzYyJXMNGo4FMJoOLL74YALBlyxb8zd/8DX72s58hl8vh0ksvxfr163HvvfeaNWXp0qV429veZrbTzJpyyy234J577kFfXx/++q//2oz1+PsLAHp6euD7fhoU7X3Om2rGqlxkYDds2ICNGzdi6dKlsG0bGzZswOmnn46TTz4ZP/vZz+D7PsIwxEc/+lF87Wtfg9YaO3bswFlnnYX77rsPfX19OOOMM/CJT3wCAPCxj30MAJBOpxGGIYrFIhwnPq1zzjkHAPCP//iP5iJ98IMfxJFHHok1a9bguuuuw2c/+1nccccdKJVKOPzww/GDH/wAWmscdNBBOP/88/GJT3wCl112GT7ykY8AAL7zne9gy5YtUEohm83C8zx0d3cfcK5THZOXclxuvvlmrF69Gtu3bzfbTqfTuPjii7Fx40Zs3boV5513Hnbt2oWLLroI69evx/z58zE2NoZzzjkH//RP/4QrrrgCv/71r/HYY4+hXC7jggsuwODgIIaHh3HaaafhxhtvxGGHHYZdLJL7UppSytzVL8WYLF++HIsXL8ZFF12Ez3zmM9i3b5/Z10c+8hHs3bsX1157LT70oQ8hCAKcd955OO201mL4JQAAIABJREFU03DKKafg+uuvxwc+8AF88YtfxNve9jZs27YNf/jDH1Cv1/H6178eXV1duP3223HmmWcCAC6++GJ8+9vfxiOPPILDDjvspfQyXtIxOeOMM3DxxRdjaGgIjUYDhUKsbv5SjcmFF16Iiy++GJ7n4eKLLzbg4aX0vF7qNeWf//mfAQCXXHIJoihCFEW4/vrr8cpXvnLCfl/3utfhda973YQ15ZJLLsFBBx2EFStW4Fe/+hU++9nPYsuWLbj11ltx7rnn4hvf+AYAoFgs4r3vfa9ZU0455RSccsopeOyxx/Dggw9i7dq1z3l/KaVeKIT+8lS5HHfccbjwwguxefNmdHV14cILL8Tu3bvR29uLQqGAWq2G/fv3Y3BwEB/84AdxGJebHXvssTjjjDPMdm688UYcd9xxAICPf/zj+Jd/+Rd0d3fjjjvueMFjeOc734l169Yd8Pm1116LbDaL1tZWbNlC5ZCXXXaZ+fvAwACefvpprFmzBscccwy+/OUv4+/+7u8M4nkx9mLHZXBwEE8++eSzntez2fLly3HhhReitfXAGO+1116LQqGAjo4OPPbYYzj++ONx2mmnYWBgANu2bcOzhc+Ussh9VhaUpaAsRUlZe/rT7MWOycEHHwzP8/CFL3wB/f396O/vf9799fT04MILL3zWv/3whz9EoVBAV1cXHn30USxbtgwXXnghOjs7sWjRInz+85/HM888g76+vvhHGgiDCI2GB8uyYFmW8SRfrjER27Bhw4T3L+WYiP32t7/FSSeddMDvUpk0Upk0XBUAQYX+Cz36bxr2UozJtm3b8IlPfAKFQgGWZeG73/0uzj///Ck/hM4++2ycfPLJB3x+ww03wPd9FItF7Ny5E0EQTFhTrr76anzpS1/CCSec8Lz311RsRuvQxUJ2w4MgQD5P2WCtNQ4//HCcf/75AIByuWwSQCmu2x1vN954I3p7e83kETenvb0dtVrtefefyWTMDSW/k99EUYTTTz8dy5YdmLTZt28fvvrVr+LjH//4hBuyWCzC9/2pnfzz2Isdl/vvvx/Dw8P45je/iaeffho7dux43v0JMkulUmbf9TrVT+dyObz3ve894DcbN27E0NAQvv3tb2Pr1q3YtWsXFi1aNN1TfkF7sWNiWRYkT3HZZZdhwYIFE7yXySb7cF3X7FvmhtYaF1100bMuxu985zsBAF/5ylewZMmS6ZzqlO2luH+01ti0aZM57uez6Y4JANx00024/PLLmzm9adlLMSbLli3DZZddhk9/+tPo7e3FU089haGhITz22GP4yU9+gre//e3PewxyP8maIveS1honnngi1q9f/6y/e9e73oXTTz8dV155JY455pgXdX+9LAv63XffbVDu5s2bAQDHH388rrjiCnzpS19CqVTCxz72MXR1deF73/seHnroISxatAj33nsv+vr6sHLlSnzpS1/C+vXr8YUvfAEf+chH8O///u9oNBoYGxvDJZdcguHh4Skdy/Lly3HVVVdhx44dWLduHc4//3z8x3/8B7q7u5HP53HRRRfhkksuwSc/+Ul8+MMfxrJly/DlL38Z5557LrZv34577rkHpVIJ55577ss+LmeeeSZe//rXG5d58eLFU9rvIYccguuuuw5BEJiQxKmnnorLL78c6XQaJ554IqIowvDwME4//XS88Y1vxK5du3D99ddPnGyK0Pl4PG41mRX9Y4zJpz/9aQRBgNWrV6Ojo2NK++3p6cHw8DC+//3vY+vWrQCAc889F5/61KdQLBaxdu1arFixArfddhve/e5346tf/SrGxsbQ2dmJFStWxOdvKeTTaXhBgKBBTXNoLlb8RxmTTZs24YgjjmgqBNLsmOzYsQNz5sxBJpOZuCEFWFxW6ftjkDSLSk9/rrzYMTn++ONx1VVXQWsN27bR09ODK664AgBw6aWX4uyzz57ysaxbtw5f+9rX0Gg0UCwW8aY3vQmf+9znsGnTJgRBgI9+9KNmTfnJT36CLVu2oFwu461vfSsOP/zw576/pmAzUoeez+cHfvjDH3bOnz//ORN5L7Xt3bsXGzZseFaU+ce2W2+9FZ/61KduHR4ePtD/Ymtpafn+Bz/4wQve8pa3AHjuBOeLtSuvvBJnnnkm5s9/EaVdU7Czzz4bpVIFruty8o/uUunw3Luv/wXvVqXUuevXr//mF7/4xRbgjzcmkhQ95pjmyxGbsbvuugv/9ul/xSvXrIQXBHBdRrG8oF/zmzumMibt6XS6984770wDf/5jEkUR1h13HI49hRZI1x9DyH0NSFPo7+6br3vBcWltbX36K1/5yorVq1fP2JoC0OJ+6aWX/tH382x27LHHaq21q7V++ZOi8uCQBNJsNq01XjB7gXhMgNkxLtKWYimF6eIETbwGAP78x0RrDQVqxgkDjZDDcrqpIhcA49q+Z8OYAIDnkx+nAxvKkiav5vIK/5PWFGDivfFcNiMLum3bo729vZ0LFiyYid0BAObPn/+yoHMA6O3tRRiGz5t9q9frfb29vTFByh/JZmIMPM/DyMgIXOfAuGSTNrxv375plVE2YzO1AOzbtw/Oi0h+spWDILBGR0efNXn9UtlMjskBIZhpmFJqeN++fVizZs1LcFRTt5cLne/btw+O4zSC4PmbGGYk5OK67ttzudyV73nPe3Iz1Sn6ctng4CC+853vVOr1+ila63ue63vcKfqH8847r3XevHl/to001Cn6G2zbthXZTBZKKSJHYloBoRDYs69vKuGFXC6Xu+v44457xbrjjvuzHROAOkW/+52rcfyaFZjT0QodRAgNzQV957+nSM6Vy+W+1NPTc9E555yT+3PvFP3BNddAOUV0L6A8g2VHsOyJxHL3/vanU5krZ+RyuWv/6q/+Kju+7HI2GnXYXlMdGhr6fK1W+z/P992Z5HI5vbOr45eWUgh5RjvjwYvhDGEOCP44AqDYkRAGQXlEpWxh4KP32bSNOXl6+g/vJtY7OIwGIqlC0cjNp8m0b2i37JS3Q1wUa1cfi0yOb75I3GT6TqlEHBAjzPORzraY4s/Q8+Gm0rjztpuP0VpvmsKYHJrP5zbbtmOYBW3LOgCzC29JZAhONCI9iYExZoORbdM5jWNvjMuZ5TvysY5DJPyZMNYp6W6ENtwo2vxMQ2uNIAiRyWRh8w1pxZuBsugi7+3rnSo/R8uaZQtGvVAbRj6o+Br7PlczyKpoqFgsODyhWhgBZnlMLY+SkYo5R6C1aWU15zLpNYKOR4vPS47H5jlluzZqAVV7VJhN0DPHpbBoTgfaigRgiP+GeXL4oH90c1NcLu9p6ez+T6UUMinavxUBfEbIZohfJAw4vMrHqiwFHVApoFcntkHPq/MxCbeMnPyzrAXP+lCdNFl4jij2RjL5VtiOIfOh44pojtnZVrR0zUNrgbyNSn0MmRSPK2hhv+d3P5nquJz0t3/3wd9Vq1VUavF9kGaGR1le5LSEYNMGsKeXxmnnAI3gEIeAfM1dq/zjHteHStOb/SO0oZrcj+PmUMxoOpE3x03R9lqKCkt76GHcWeTjmjhEpqvaATBSoeMKlQXbcbHhu1e+A8D3/yS4XABAa33DqoOpFNBjytxMZlxruMxDRVdDLoY/7jM/pJsn5E671iy9phi0zOvI4tUHUYPmQ7/kKpd8GwAgqpd4BxEWnXAqAODmBzYAABQfRMqh5oe/f9/lmLOACYh8Jg4Kaai2PkXZ/UefeQQA0DV3FZDiCz1C+/j9rTe94GLOY/J4VxeR92Q4659JOzG9LU+SIGDa2hrdmBYieHyTBnxT6kk3l8XkSI5tm8SkHs+0BcDhGR4FkZmc8iCQxdHlVx1GCMP45hy/75cSNGqtx9762mMBAGkmhLKtEK5LxzVWpsWo0uCHO6/0tuuimKPFc2UPNXnN50XOHaaHuzVKD2MVhkDINwwPSciLrSyQAUKEfFMKVW/KpXmYLVKHYrYlg70VqgraMUbXfrRBE9nneHAQvPhyVr6Jv9Wz/ND/BIAuJhlzPaDC12teD82jBhNIQcbOtRFWhgAAw/1EczsyRHXyns+ASBZorcdhCV6gnmVpNR/JYi8lwCka/+65y5HN05hJ3qDBD7zAfvHhFjGt9a3MK4ad++Jxn0OMv5AAoCyYvLYio4Bf3kz3z0/vojlxf4m+PabpuBem6NxObh2D00HHfNujtI99PEk8fnhqKBP/FwI9j2dSsZ22e8gyCxecTCv5scSoC76tIRXPOV6N8xp4aAddxwZ7Lz//zte/N5UxmdGyRZextWbO5SgKoSw6YKWElY1GPWSWt3wWGBylhTwtC68rT0OaQF5AN9MpR52DW75L593GE+fo11PTwM5tvwcA9D+6E+j/PgBg6TxC6tt6aaKPVp8BAFx++ZmoCekxszeWDQc4Xaj57bTvN/71P6IlTw+Rllxn84Mi6IGRbBTFHORm4fQFjfPnWpsJZJnFn9GWLHA2jZ9jO7CY2S7Ssrgw4hR2SWcc+uab1Ba2S34f6pg5McYI4/2oce2Mz3aCTRg/701tf6GYQT7PDzz+mwdG3byYpLIZzO0gpruDmYs7y8x5tRF6uEf8sBvNpBG20ndVkW6yVI5qlzPyEK3XUBsjnu7GKHt7NZpnup/23RK0YwnvM2Ae7DCifQ2zNxBwTV6kY7ZFe5qkY8UUHaPH4xIqBylhWWRedGmS9uoERCrDFZRHmEGw1M/nRscfF5iKN2abxVkxIBDAJfMjCgMgEk+HFzQ/npcA0LfnKbR1d/KW6bisNN2Paan/5u+moRD41QnfacYefJju2b1lWqArXohFZaJJ6Gxj9sc0HYNfpe0PPKXxg9/TtX2QF/LIJe9l+aH0/rKz6LqesLcbj/C9v3ULgYneMdEaEBAVhxrkHrUZ7FSH6bge2BqgUOBxztLDdh57EkN1+k5XO12PXKOGkYCOTzntvOWphZX+VOjsEkssscQSe5E2owjdYjTuKkHYCo4hKmK3NkNP7jDHaKoUQVmMCBjF2kpCBeQuLZ5DDTTPbLwJnXoAAFBYSNs74g3UnaU3EunN3kc/iW0b6Uk77/VEG7B5F7nNtTohDq/hw82QF2GxWpLNHMi2pqd8J7vzKWsADj897Yw8TaduEupoMKKLHEtAEkLhKxcebaOyYxt69ohRUkrixRLP5FigG9lI81UORaGI3V+bvSHHcaCciTzoQiAV8G80xsXTo4mxV208dkZxahxnxzRq9Bz24Gw+JscCShXmOOfjc5kfPcfx8iUd7TiowPHqfaQ6Ux0llNpwuYklT8jNmTsH2TkUiku30LXL5Og158oc1VABIdnSXkKB/U8QFYQeIY+xNNKPFCPzucx/3UgT0iszh3aD5w8NNYe1mh4RMpWiuHM6R/v0yjWw84V6ldSVhgcI7QZhL32nNIw6NzRpX9rquWSQkaXDyNh2UrAY4us4wcIvccA3ktBng2PxErfn7QeVEYwp+qzYSSo9Kb6mIX/H52Nw7BQqEkpMN+/NHX0kkdcukVOLgPZJdReyxGzlePk1DwxiW5ngcU3ROXQcQu8/9hfksR3Nt3Klt4r/upFCVpurtGEt94FxX7TJdAspncx/mf7hUID7n6Rrs3AezbFzltBrwHQH2W6aQ8vcLHp3UtRgTndzXn+C0BNLLLHEZonNKEIPDOIUrUyNdEaSR4z6RE1ljBBZFPjgnBRqNXqaBozcMilCCme9+ggAwG+vug4ZhzZw1BsuAgCMcWx0+Tp65G68oQjVR79L1W8EAHR3Eo3tTk5w2bYFZRGi8JUgVD4+fjqnFD2tlyw4HL6mf4dq+s/HkJN0yrLhcv5AStR8X9DyuFi1yW/SUz7Ng2RxNU7EKFz5ATSjF4tTzYIqFaNH7dURglESI31BbQ4nZSIrhImVCySXbJMpaTElJ+NQe5MDMf5c2FWpVapoMIoRTynNY9SaZ+3LTB7pXibFKhFKzmYIkQftdO29DkLlfnsBXp4VJ3N0fllG6gF7gbVaDYrr6tuWHgIAyGfp94OPk3ap2jcEjzVEszbHbfma9TPSbXBSNLQs+DzHp+G0sLEmKQhNBpENAceDPil0OS5lBHMuzcna0D6EXNUiXhs4Pp7K0jHbHNfWiAwyl1hwXOQiCVAFy6F7ymJvRlVpvAV96zCCz5qqJUXXJOD8TQt7SZ4vwpwptOTm8L6aH5hHdlF+YOkC2kbeASbn6MW5kFTI0/0V9Ib0rRWH0pj87wtoHh3JYXzRI/3hzx+CXz0cALCKThs2V1rtYc9nOIxQlqQ633eaqzxk/BxLYWAvjdP9D/Ea1U3H3LmINpxOCap3sLCNog5W2FzFQYLQE0ssscRmic0oQg8ZIQgaRaRR5fiZy9qMKea7CBjahSowT51MmuPa/Ng7dDmpldx2EyluO56H3DxSPVlwGKH2OiNWK037XLXuPDz9868BAHbeQSWIK04kFrU9vfS0T6czsBhRZLmuXcquIjkH9jayuVbYASGmp3bfy0d6wpTHxNSJmKYTCz6Pj2YdVOXIeUulgW/+LbFJzfV3HiNChyspsrk8Ui5XckgMlsfR5xix543A96g6o1GnOF+NM/suo7eU6yDkgK3E1yNMRFSax0YpK4Ym04DolXqNj50V4h0HNiNGidcGDFysDH1HhREUI6cSe1dRO5Uvppn9MCqIZmyEVCE1YSwiqdXm2eamHLiMZH2GeNYCjgezp1gq1+APUuw636BxK+RovuTYy2jjss8RPzLz1g+nB9FVnca/rghqliqDaG0jXd18C5UE2yEdx8BOmsteowHF5xSx95HOkKdhcx2f5GgiAK7ozcpOJ+nZKqWMFx1xlZSbJ+8mKtNYaD+A5nkYMCtjkKM57XOc3Hbp7wqNOO4cNY8vU12E+DO8kj0bni1X6NhvuZ088MGSj46VdL3fcjaVgL6WkfnkBfFd//sEvGvSZ6VttL3fbqFt/OK+YTy0g67tMHtPPt/PkqcIQw2X8z87d9FY3MG95Ctq9I/CKrou5baUKcn2mqwSSxB6YoklltgssRlF6EozfahmVBnVEUaiDk7oaqzG6JQRcBiEBh0UWOE8iOjJ+Jp59DzatOl++o1TwavP+xsAQC1PaFSaJiQzfexpr8JDt3yL/laifaUC+n1HOz0hvUrJAMs6dxlKnbI4F0FASCgsV7Dpbqpr97tWNz0mDqMmPxI+59DULtsuN7aI0n0odcx2XGkSyfFw5Qp3CGbz3ASTLyKVpmN30pz+l9ptvg6Wb8H1uf26QrXW0Zh0FdJ5ZuAixR19mpusTCu7qUMf16ByQHfq1C1f5Npq8ULSaag6eye8uYD/ZnHORHtlaI4Va67zdedTbsTuJspcx6XjzFqhaYbSgpa5ttpiZK0Rou7T9kLOPfiMSJ1ORv7zy2iM0Xd8UYTP0lgX2XOQuVoK6rB5Dk631Sj0Ge1Kl7Dlwg9p//Uae2Ga0LvPcW2lFbRUlPD1jzgv1ODvRFzDnspmUC5RdY4vDVGMMLNZup9sO0Cd54gvvLfsERUyzEMejhqPUZB6yNfP7uRqsYjuqxAtAHd8Ky1tXVO3Lvagn48x5+GttN1Nfcz5r3NYUKDfdXBHazOVR8VlNJ5nLqMKlKMOa8WPv0se0XcepzEd0nFeAuC8E0+14TIdx8Ocy1vNHUX+EH0+kgY6pZu+yaa9GV3QW7I0iHWPb0a7ECdK2cWvVenGMK4+bOTZbY24++q4gylJ8fBOmnyaJ3X70rXIrCL6T82TzE0JmSuNppMLccixbwYA7PztDwEAY5soZHPMG8m5+sXNv0RHjhaVlEvHLM0YEd8cI9yJ998//ylOOZHUT+atfHXTY+KIWz/uAWZ6lC0hiuYEp45b0C2ZgqG4rhwq4IXE5q49P4oQceOGzUuJhHBCX8o0vXjbDi0MmRyHvHxaILSv49APP4SM6LF6tnK86VNKtHNDiKnNtFPI8wPK5+vg8cMkz4uBrpXg8IKQ4xJChxPuGYfOM8PhvKylkLaENoIbiTh01eCH1mithJEyJeYDvk3cHI1NVwuHLLq6YeW5PHA/lcuqKh2fw81vvk/vAx2iyuDAj6ZXuNg6hztEeSEdHhmGz+GpiBf7sTqFzqqSgAXMomxzErPhUWjEEyoELie2Qs8UJzg2J/r5gVTjBGAqFSAKuTCAKQgCPscGv6bdlKHMkFcpcfRY9CHtMDhRGiGH/hyn+YABd9GbBT1C3O0rYaNHdtF47W9Iq34Re56m6/3rX9PDac25tKVlvCIGVKmI//7vZ7CjSolmt4N+s3wVXb9j1tDxrpzn4NQzaU7ctJMaguolGr8yn7+yLER83bLcWT7ADY3RUgIcozXaeWsGqHN+v8YPwjlTrIhOQi6JJZZYYrPEZjbkYkmjCD2pWtMu+pibwymQK+bVOCwj5FKOZZJwLRz2WMElZk//9lYAQIobj0485WQ4nKCRWEQA+k21+jQAwE514PT3kOzWZ+/4EW3XJ6Th7X0AANCda8MA1zjlubwtI+3kkHI0enK+dtUCLFlF3CPanQZ9rJrwwsRR0sQxsTxQkLGChs3IXp7IkWkSYbeXyw2r9ToU6rwTDtMI9QI7AGEYGrTtOtKgRH9LcXmgrnnjMmXjWsXHfTeaRHhFx/zCQzDZchnxJDjUoRVsHaNIAGjhse7gBiPL95DmY08zwk8XOUnLCd/6CG3XggWLEXooTVfMpTPEKGrb/kEMlGkuViPGf9xCfuQKSkC25PII2euU8QsZ4WaYVVRKBSMdUzlMt7XI43nu1dkTyRdRrpCL71c5/OUxMjaJV2VCbNKY5zJdgM1NNQ0ua42gIfJBdobGw+ZQqOdxm3/gmxCLeDcpfm3wbMy4KWjP4/Pn4/LJ2xljXp0chzzcjEI+xWWlXqXpMZEFTM42ADBYZq+FmwAffJKuY4UTllFkIeRw68YHyXv9JJ/Dx0+j9WMtC1u95jUL8YnvPQYAeOwpLtPcTNfxxF2Eyj969ny0tNPYLuqi8+7jRGwpiHmUJPlbK9E1So1yuadFiV0pfdw3AiiPIwGM0FdOcTwShJ5YYoklNktsRhF6ieNwgjT3DA4ApkyMXgtZzgIE8rSPoDn2e/gSiiHuLdOTzdIU/2o7mJ5fucOPGBdMo988+Oh2AMA3vvdxAMB//MOnUemkJ+3Bx5L828A91wIA9HZqGDni6DNw5723AQDqHN+zfEZVnDQb0YRu7t39KOb1E8m+mts8QteTWoVtyzLMiVpKn8Jwwnd9X8NJSQkoo3dXEA8ngw0Kd2BxQ0qpSvE9l5GmxEszaRc2o78w4uYtRu82Ny5FOohpfAWhQ9C8lLZNJqGdpjElbTpNaKkyWkbgcRyYEWORE7r2KMWDU1FkxjDH8faWVkZkJWbe7KfyMLtUM8catHLOYAHFSQNmZNz5+C5s3U/jNRSyZ9RG33U4dryquxMqJSWltAtBSGkeowxnX50wQlqm9jQ1Re0GHVu1IU0rKUPTKolFcwV0nNdQQs0s9BBMXCcJVVM2iHF0DnwiOpiYjIayx1EPc6LTdErxOFn2OOoH8TLFDeBcVED3UVCvocI5BceVdp6p206+NzrYC/EBlLmpq8Ba8Q3OMwg1bhRF0Nxc16jSHHv0AVpLPs/J8fedQej7Natc/O2bVgEAvvgj8i7uHyRUf/deZl/UGoczq+IRR5KIz3076FqJYPT46l2biys1l796Ug7M+b56RePJAfobBzBwPKZGXJYg9MQSSyyxWWIzitAjISriJ3pgOUhJmZjEcLllNp2SqgMbGS63KrST0PGOX94KAMhwid4rXkvyWfc9sxs3/t8fAwDKdWoiWHMMVaC84zWn0G9SLjyOiR53xjsAAL9+5iYAQHWQnuT5/dvRwjHqUNCsodHlbD8j2Ifvvhe5Ngq4rV1+PH14zKFNj40tlSNQCCXuKM1Vwt3N46csjZQtMU3mhGf4J+gxxWOWddOIRIShQa+ZAiHNSonRrauMIEGDPaNQULjRhFAxfa6UDMo1Y1hgMXoOoMfxaDePRhsNacqgnTeqHiIpA+PSwXKVttslsFcpUxaY4QqffJYQusdVPhHHzXUmDSUkXC107SKHEJlyhFqghAU93EbP7dd1Lr5xJIYchcZjsLm2wrLEq6rze/qNnUoDzD/uThNGBVyVIpxQPrSh4hXPSsp9ZdwjWKYiytAxqMl0xzHCji8be4wx5KcXSxnaWEMDbCgfhI8C0JPyBEKZIXkRh8dNhS4adUK+yl74QkNwgBUYmctCZgPoEq+J5+7BK+me2NhLpStRqKBZx9Sv87xv0HV/YBMd57/vJY/iiUNdbN9GWx/i3AV8et+/j0t8owhZ9gpSfTsAAC0RzacBEzKwzBgY71DI37j6Se7BdlXBvjIRwlWGVjU1HglCTyyxxBKbJTajCD3PlQTVGmd5odHGdb8NHQspAKQ1CAA5L8TqVkJRI/dvBwCkI0Jccw4h6sz2o44GAHz7mv/A6459AwDg6p9eAwAY3vorAMCrPvoVAIBnOahwzfv+xpO0j/nLAQDl/aRC5O8fQMsyQtl7nqCmI9flZhVGQNz/hIFyBrf+5pcAgM2voA/f+Y7zpzwmAoBMrA3jYufSuGNi1zFFrqgYuSI1ZoKfdHyOubIKDR5vR8QQHKGgFRGGIG7YMaRf8h2u01ahaZIQc2yh6uWaa8THKyRP0TTixVVG6GMV6jMIAtuMU8jw1mePZoyRdrvjGGWMOosdpFJEA+Gwmk9UpWPaVx4zzVo2j4lEjxtMSjZn0Vx0c5zed7gGvkDv53KNfkt5CAHHQSV7YnOtu8+NXlWum29YLiIeJ2mia9ZS3LLf8CSHFJkW83yemqiqPnldDbBykY5ifVdzphxTn1RhZYGoj4G4IcYyOZ6YHtbkSmzxjqRhUOLl4TgGMtkJe5BFyvEoydXUK7FqVLb5xiKH8wlCtZy2FKScvcrguL+PciGWcMkpZYjAHENGxt5wiY7rCZaafPzYGJGDAAAZv0lEQVTxKiLu9QA3YMlpZnhsUjpO3S3g7bUYIj3poYkrfsBrW0sLeYAWz1Op7hrY3AeMsHc5tJW3fPTUxmNK33qJLJBJwokiO61Rl5I8R0rgpASKJ5DjYNHKRQCALb++kT+jK7burecCADRL1PV09uC2PlpcXYcm9Ny2hbxv7hKrhbCZS629k7m01/0FAGDbQw8DAGo7d2NBJ7ntQ1xeVR4l90q4U1qKzBpXrWC4j4517C7hcpm6xZo/PLEsZZLGcv0jWWXlHokUfF+66+i8XOl0ZA4axUlhPwwgVNE51nF0mEfDyTNXTGgbJkDFZW+iQymqQVpFJqkmjyHpWJRrJvEFNc6Zn47Qs3K4o1E6NYMwDmXweWpejRr83s4XUC1Llysdey6ihbxjMTHXzVlKD+6+kV2oBsJVwxR63FBV5zFe1agCMhZym3Di2e+j5Gp1qB+DVS7942O3eG5WeWUsi1wZQlO2O13FPsXHbHMD1b7+XkSGpXQXAJjSTXnCKxV3wwpAsKQzc1JYJII2HEpZkdDTkrCUgwCUJSFBmaBy/eWBHo5bzyVUw/c3JyYtLofNKhcBszSqcKyJ0SCrTUpG24gXV2FpXdTJ3btq1PzG8CUhBlIA4HN5pm+0YVOw+IpJSSr4PtCTdAGAcffC5JATtHmwWhbNmRYusW7jzuieNvrNmLLRy+XbW7bfzVuY2oKehFwSSyyxxGaJzShC98zTj5kMXWUUUkJ2UUUMWBok1i5egW13sVBznRzbeUsJseeXHAwAqDKHx/pDzsHXr/pXAMDc9iPpu51SVhY3zmgRd7UIlbUvpSftkiOIi2X3fY8Afawb2U66o40Gc2BzqKNeIjcunXKRzwg9wHDTYxJTiUvCMRacNe6goFJBDdo3iWKHEVW9RmNkjUpYhiGVkzGIV5C/Y0l7O/3GawQwUQBGW0riPIG0h1uxFyX75OYRbeCYILS4hFBNgyNe5kRLC12fkYExkzQWqoNUWnhlWLmoYyHCym4AQI2bwhRzlecdagTKtlJSPcimsX8HhddGR0jdyMmwWHeOmlxa27tRkEYc9lYq+1mbc5Tb/ft2IWTX3GI6gAojz4EKeYjlKEZ1jgh4T5NtsVqmYy2zInw204WIRcxdV/h+aHw8l8sDGx40o9EoiDn3gThxKWLYNmzjhQUe66dG0qBH8ymTsuFzOEdzGaXEIGwrViUy85k/czjc6vCSk8tSGDWVCaA5OVhulJoek/bUxKSoi/ieEr6e41fRPLr3bpoP/X7MDCqhFik7DU19ISf4/QiuJdxTPH68RgUG5ccmxQByD8t9pBSgWcWpJUvr1cI8fWfNHFo/RDW089CD0Jqn43vk6f1TGgexBKEnllhiic0Sm1GEnpYOajem0vE5QSnqNA6j0CJ/eVFPHru33AEASDHJ0uveTQ1BmtFQxP3bv7vnm2gdo7Kht57/RgDApqepVCnV4DJI7SFgDyHgUjPNCLbnYOJX33H/Zoz1kgLM0YcRYrtziONebYTyIo779Q+NIGAEJ+rizZggAsNcqCNT6maU1qOJiC4KA9SFBZERimHQq1PCTI9QPNJyI9iceI7zFIyA89So5TteHB9ktBRyu7zHPN+2ZZt4vWNNzHcEBpbEMXaTDFXj8cvUTM62xoROsC3UhdlPlGD4Go5W6Ro+40ToKBIS8/ZRPPnem3/Ov6DvzFtzFG0/8jGf0b/v03kODu8BABSz9HlP6xzM47JF1U/Iv38XJaieeoZyLaM7nkDKJlyV7iT1mR3MCT7E5a3gMbehAEbm4TQbi0YrnPvgKHHYqCCyaM4VWfWnUSXvwWIvLIRvEoA+X0vLZkoruY6cpHMsBw6XelZqhL4Vb0eIuFKODStF329wuZ1ij0oajLQfe2iWzeyKGR4nJr1Liyfua9Q4K+qYBPXUTVDtpBTsBDtqJR3DUQeRF/bolgieKHvxL8NQmrUm0jO4jmMa+4Q6Q8jJbOH/B8w8lzxhpGR7knQN4Fg0/u0tVFL9iuXk/S9hmgGRQtXdFopj9K4l2/VCQzDBEoSeWGKJJTZLbGYVizjmVGRqXC9KwcoKNSxngDnwddRaqk7Z+1SIRpkOc9Fy0nbU3RQflx6KIabjffKR7fj7974PAHD9TzYDAFYeTb+5835C3MtWzcPPb6FSxm6b4uCvPIHodBccSvtsX7wWpW1E1LXlAYpRFtvXAgDSLsXhihlBGg5GmAxoZLT5GKBB5lKqOO68YpN4dNxa77GHECnhg+bKIUY7EcdALRUgxS30WUbqIdMWpJhL3ApD+Ey/6nnMuS10wQF7Tq4Tlw76Qo3K1MeCi8Yhdcv8s/kql8Fh8jIyjG7DIILHcyeTo/MUL2usTh7Y00EVC7nyaAHrVgZjdH0fuOUG2k6NENL8V6zFnG4qeV22/BX0N5cJq4RKNtJo7CBa5bHHiZyp9/6NAICBzTSXisjCK1Ls/JkhOo69DTr2iqnSYiUurUxDVjiNZisAyDJ6bgxRlU0ljBAyJvP4etlMfCdkbZZjI+LqGMXEZo0qzY00V1nkC4LrYiKvYiodf4Y4F6Kh4QrfPB9PJHOGK36gYZR6Ujkan3wbebq5HJPd8S49pJBn9O9PI4ZuqlP41cKBVUQOx6MXr2RKiD3DqFXJSwlC5gdgzy9OB8UVLBoT4+qSMzAlneP+b7dy9Zs5HtEMqMO1yevJ5mhs6rz6jvJmmV8Nnhdh9w7yHMu+EARPzRKEnlhiiSU2S2xGEXqFUYTU42qloTn2K1UoEcfjdJVIbmo7bkFk0e+OP3s9/Y0bY+pM8PPdH3wVAPCBt52FFKN3naF69JNOpBrz+7YQsr76mptw98ZfAwDmc+xw+RyiB5izlFDboSe+EvfupDhpfYiqW046hSolbr+fYpRjjHYKLe1Ic8VFZ6H5GGBc5TK+fnUisZFlTYTsYagNLYAQbIFjqUIb4IUiYjCKRpUwS7VE9biuKzqHLD7gNSYotgOAZn1HzUIHntLQUpMujSDyXak/N3S6FiyJNx7obrywsVdQ82Pl9EyONUCZ1tUIR7CGZs3V2B/SsWbSEwUy0hVCOw/etAEAsPvhTehZSPqgXYuX0G/aKZAZ8HGX+7Zj12aqhNm/i+LrY8OE+IU2QLe0YR/HUXdxnHVMGnA4Th6ayhLHtMqnUtOrRA/K5C2WBf0WCmjnipM65xvqPsespWvITRmdEIPUNV3rOgt4pFi71XJcU/kS+1UTvUMdRTElLlNJBLxvk+uxbCj2YIssytHJtdaRJQ1n9NVyo4GWVlZDUi9iOeJz7KtrWFnaRzf/SapdjjuajuE1jw3h9j00hrv5d3FfBx+DFlKywMxlzT0eQpDgsJfY4sPE13eVaExKmnspOH/TsGt4xcGU5zh6NYnwrF1Bng47jvAkVK+BMW4GLLQf0tQwzGzZIvOElJkTIeWGJnkjHW/HruT3+ygR5egQi4+gRdpeSAPh8YCW2XVs9NJEn7d0Lcb4BmuwEzZYpwn+yKPUlHTvfY8iy+77CC9Iv7r1egDA2xb8PQBg7soVSHcTg6LTR+72U7eQusjioylxOtJHC/2YH8JxhFdkGk006tluoLgpBIgTXGJaB4gktBIJEyOHQfjGc2xRhFGIWOC60aBFPrDkt1ySqGLJONNwyq8RT9RQKZMM1YEIZrN7L4yPtnDNWONuiObDC0p0/uQ4dYQ6k3ZLFaXNfO8h8/IMVyJYLEA8xN2teS5BbOdkaYoTxcM7nkFpG4VNtoqaj3TcymIVNkzXbSASa0VKqnnckNUfRtjHicF+4Q1nFj/DAy9MmVEIOM/edDJVazgUmshzM1RHRwuURedUDGhxKI/R+xp3jHp+BPADyITsuGxXBSKfx6DKScESVSrTRir8S/Q2CnxTZmgeVnx8ho/ITcMtsOwf6wiE9kSQIkBEKQsWc/b4fvMPf8lX7thLC+neUKF9CY2TiPzI43PRXLoo7zttHsq/IEmg6h7a96BcMGaF1IqDJkqbm8E2NweznnLV5s4tVYywelpfSB27YxEze7r0+ZpD0lh/HC3yrzqc1cQ4LJpS3NTGi3h5qIFiVhb77U2MRhJySSyxxBKbNTajCN1md8uRMifbN00uaW4b7mw5FQCw4x4SXs7aCoesZrTMYRlJkI3sI0ayvzn3TQAA3wIqJeZC7iT36qFHfgEA2PjoXbQ9nYJoLMqT95Ht5FKfsINYF1csPw1rX/0aAMCDPyaEPlYjhH5YD+mZSru/KlpwGcXUJSnUlE1UCKIszMTSqQN+YVlQgvz0xARlwBuyTEmVCzBzpBYuEaOnaWgSDXdHNK58EgAiFbdIR9KMYY6E3XApVzMUi7b5vZ5GiV5aGBQFoYchQtFcZe4OzX60IEpXR8b1H+Twkd1CCaoal5Pm5xBqDLMuUKXvpBnhuVzG5op35BTh83X1WafV57DPCHs6w14FpUi8FS7lY5TpZmifjqgFBTH6DKdBhwAAcxfTfRCOkH5pBMCvcoMZN5JlW5lFUlMYozZaikMhzE1TrxC0jETb16gK1REy3cKBV008ySgOE0rbvKhncXLTSmWQSpM34zItR4rHIZCS3AY3R1kuKhxKch0pQpy6PfkUXYtHB+k+KC4soE0cz0mrm7xfuMLF355BoaD8DYSkN+yicFq1LsUGHFaLKC1KJuPIL8z4eelVD0DxWJYY2JfZGzt4JaHv1y7JYgEraKVY67bUT15FK5e+eiw4XumvoJv1F9JNNismCD2xxBJLbJbYjCL0jOg3SnLNtYx6yxomw/Kf/h19l1GDXWzDE0OEKPTvbqHP+GnP9f0YFNa/vb2ILIovHrqQEp1hmWLxpx5M5Db1gzJocNOHz9kIq0alZn13ERpvDRchZRp3uASuRGVpu+8k9sXUMkJLo73bUWjhksFCvukxMXFyiaVrbcrapOnIIF9BBlqZ32mjHsMIVnIIpnM/1guVxh9RuRFUrrUeV5oldJdyfIbqEZMjnJLcFm9BmeMdx7Q9nRg6oxtT+qgVFE9VYdULZJ/s2aXsCJpLL8sS42T0PMhx3EKGY+sFGw7HoZngDpYvsXMJmDoIeF5VOD4+NEpoqcJlemEUxQSDMkyRsIayNyqEXI4yDTi+qY1rznLc0JPrItS7dec2VKqc2MwQ4swqQuYZ1ulsnVOHThHyGxqkNnIJVUvDVRQ24pMw/WAyR8TkvUIk152541OZlgnv3TRgW0zuxmWl5YDQezpP93mtwUyaGrBZBzeXaV7x6/eP0v3dvYrYUTs7FIq8zhyAVmU+tQCLl9A9e9ZrWanqXhqjAa6cfJz6EzHQCMzFjQ6guKB3+4ZzSNs0lgdnaDvtS5hw62BC6B1tGSxoI8qSah//nteWOnsrpWG6liNDDRFcw46hXVMciec458QSSyyxxP48bUYRejY7UW0+iBRauUX2NSdRq/4vr7oSANDJCt3hyCi230BlhpiEGqSwP5SmB9cxjUmayb0QjMreARAxUUooQieBxzI/wR988r/iskB+CjvccFLpo/LH+SsZ7ebaAIdbsiU234QZ0ZcYfo/jQRfCLomHj4+9TkSxYnFfhDLvpYpAUH1oWtBjrVJB20Z3Ulq3RfkmimDi/eIRCdmUJQ0W48oXRR1IylKaMC36kFxNoRB7A5aQHhn0LigzMJ6HHJcv9LdSrcLQtGJbUEwXEdXos8EhmidDHEO1nDSKHMt3mV5BKHtFoxNKGaIymZXCaiGVOjajVuU4Zmy18Gs3adVR8gyqPCm75yyBHiTCrnSWivSyGYKYfpU+7x9pIGJvxFHtfG50jm6Oj4cJ52yt4hJHoZ0we2eahwBI52l72TwrXzF9R5pLFVsKBUhlpmXJ3OOyzmGK/8udom0HKb62kV9pckSAOpfVckU00ohb6IXSONZjInMApLkEZu0aOq/5TLH8yBOUK7ubA/HPjEYYGGTa3bp4cUwQ5zKlgltFazsd+xGshGQXSWmos10oD0K0sMxZucQUAIvpb/YorzVcMTVa3Y8KRw0GaglCTyyxxBL7H2kzitCrHDvPcdVBazqNLFOwDu6hp5afoUdnmVPSBc+HJfSfUtAvNc5GN5HRZBABHA8UJKeZiEgppvzUOVgc3zSd6owIhYQn0DpWcbcmwnhV6uPPWRWnWIAOCXVANx8DnGwa4+KWEs+EUHzGlJ8GmBvyH8nASw27VCVY4wQnJD4+OSavY0TOYyGNKVKXHUY6HjAR4zBHPbFmGVDx+E2joKPIjSZj3OKuEcXq9oy3hFJANGkrtToC6W9gTVERZRAlJSHFqoQNRNwYxgAfI4x6+7lSyXEjWIzQC7Yo/Eys31e2jZAzCxb/zeb6/4Dnui+NNMo1rf96mjH0iK9/SzvFXkPLQ2tAlTsVroZgBwMW41FlAyklAhJ8baXyJCseDZ9PoE1ht+QLQqFTlmOAbTzFDN+HLW10PI7LeqzpFCLWP5WqmYZPnkOgheyL69PDqqm2KTeaH5eAm6N0ibY/+EwdYy55MrqF9rF4IdWGSw1NB+KpylxsWNBKY9PRSg2NR66gLzz48GZsfHQLHV+NPJNalbYfVagy5onenXCL1ACUXUavSxcRiR80IW1vtIZdHlNcp+g11Rfw8dB2yyX6rt1WhBPS+bTlm1tTEoSeWGKJJTZLbEYReo7j0H5NYqMRRriq4MaHiQxr1bFHAAAsjkWlUxmkcjERFgBYqYnxXdFy0HYaNreN2yXK6IchPZfbWeii6rgIAqpdjfhvCFgQwaLWXCvwEDFSEXpVHTBpk8Xtv1lCgatzGk8PM4KbhriYhGBj8qMIcc2vQGj5TmS+Y2g5DXKdiJqViRyGcVm7QdLWxLd6IvEXfYXrskU3Ehq+N1HKTI4vLsLR5lUqXaYjQSd1044rRFwBGoygRfZO1OgNJbDtmM+kJl/auRFI7Ft2oA1damQEGOh65jm+HEXKxJEdrqxJ8ZjWODYf6QiBdF3yYBayNG+FCMtjDxRRBJeP1bWnh6NyTDJlMxWGDgM4LIvXxt7E2Bh7NTzujh0h5Bh3RY6bt1fMETxtKxCBVn1kFENMkyAlHBZ3vpquYAtw2APK8pgVuRO3ytsvjY7ANpqyhDpDRux2iu45i8erVvNRE41hHrtmLMPUDxkWi9izfzdKTMqWXUZke0sYoU9Uj+V/T7oUuQLd55IDeM2cDnzvu18GAGzdKpOOkP9BrD/bM2cOMLwOALByCfWpCNX3CBem7xwp45DDiC63tcBdrY8+TsfcaOPt0suKhfPwzAD1xvh2c0v0jC7oDU7GSfKw4WtzQ46wGsvd91MZkvAww4/ihYdvhILDyYhQdCGZv0Q7CDQNcjFDk759Hg30fJ8W66H9D6DNo8RHZFEiZAhUWjV3LpU65mtD2FOnC7J6Pk2DbX10EY5ZSd95dDe1/rct6IZmVzOnp8PRMXHBUypOfZoFUkJMYUwJYBbKiWu+sUjY47Q21AESTrFMyEUEbHXcUBRIHR/9xjVNYBZCSX5OKrWcREIHrSOzyFvTiLlUmFNcShNDWCYxLIkjkbH0QmlNV/GCLU1XTHngybmJ8LVtx2NhSzmk/I2Z/4LAKCdpk8ETWgU6lrofmIeGlJgqpmDIcrzHiWSuRiYDbk1zQRd6DJtZCcNQw2HxctHG7Gyl8sW+XprjvpNCqwhr8/0n/ONSHluuc1LfslHgcGi9TgtlnNKOQy+SxEzLBWdeHZt/Uy2NwebF3lx+5kWXKVOP6DfZXAae8N6Hk2fxC9tRa+geTnGZYL5wOMaG6aE0lmGmS/7uVJiWTAKVySYb1RSCKjNECpBMUUhwfjeVPFZhIctPAAmnNTwKBfXuIyF63dYC7vBHJ/PbtPfTw+6Xv6dyaStD4Z760GYEEV9jq7kxSUIuiSWWWGKzxGYUoVvMSihli0qHCDiLk+G2YS8UtEDf8aIQWsq9ONFU5cSKG3GjBp9GPQihInra1yJuc+6lJ+TgfkL+6xbPgZWmhEW2yO2/HrlmjTIT9gRD0BY/z0N6Git+4oK9AxEnsnWIdkZyahpli6YU81mArNITvmLCIErFZXyCCOS7pmxR3utxxFsqxv5ArNICjXFJR9quw+Ejxe6l42r40r4u7rhhWaSPwyAm5JocSmrGAg5TlAS5OQ4yomjFZXANRt8hu+uObSPLLnCe55nHjWOC5kVZKQojeKxDC5d/71BiKsVKOg0/Qp3PNyOc8HK+lujUAhFTL8ggiM6kxwOQ5mNytI4vZDC9pKhVp997DhOTaQ8NpjDIFAkRp7kIoOCSlzMW1lCuEdorMSVCSwvN+5DHt8zbsFRglKDiI5wY0rMUUGPlozKHQG2mEhBiKyeXRUuR6wIZtdeE0KvOhHqsNOWlisabzKea4/4GgEXLaD92G19k20Ybq/xw9zyaafeTglJh/3/mxpsQlOjYPWZ39Zn99P5B8gQiWyFr3w0AaGyivR22/AQAQLVG4Z8o1wAzSKCQpe04PDfaczRuewZJw2GoVoEKWXnMTxB6Yoklltj/SFN6Gq3ZiSWWWGKJ/elZgtATSyyxxGaJJQt6YoklltgssWRBTyyxxBKbJZYs6Iklllhis8SSBT2xxBJLbJZYsqAnllhiic0SSxb0xBJLLLFZYsmCnlhiiSU2SyxZ0BNLLLHEZoklC3piiSWW2CyxZEFPLLHEEpsllizoiSWWWGKzxJIFPbHEEktslliyoCeWWGKJzRJLFvTEEksssVliyYKeWGKJJTZLLFnQE0ssscRmiSULemKJJZbYLLFkQU8sscQSmyWWLOiJJZZYYrPEkgU9scQSS2yWWLKgJ5ZYYonNEksW9MQSSyyxWWL/D8nS/h9Qh1YSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x360 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualization\n",
    "#random select images and their predicted labels\n",
    "_,ax = plt.subplots(len(ls)//5,5,figsize=(6,5))\n",
    "row,col = 0,0\n",
    "for i ,idx in enumerate(ls):\n",
    "    img = X_test[idx]\n",
    "    ax[row,col].imshow(img,cmap='gray')\n",
    "    annot = 'pred:'+str(int(prediction_test[i]))+'|True:'+str(y_test[idx])\n",
    "    ax[row,col].annotate(annot,xy=(0,5),color='black',fontsize='7',bbox=dict(boxstyle='round',fc='0.8'))\n",
    "    \n",
    "    ax[row,col].axis('off')\n",
    "    col+=1\n",
    "    if col == 5:\n",
    "        row,col = row+1,0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extra dataset size:(10, 32, 32, 3)|Datatype:uint8\n",
      "Preprocessed Extra dataset size:(10, 32, 32, 1)|Dtatype:float64\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "IMG_HEIGHT = 32\n",
    "IMG_WIDTH = 32\n",
    "\n",
    "def get_list_files(my_dir,f_ext):\n",
    "    list_f = []\n",
    "    for file in os.listdir(my_dir):\n",
    "        if file.endswith('.'+f_ext):\n",
    "            list_f.append(file)\n",
    "    return list_f\n",
    "\n",
    "my_dir = 'extra'\n",
    "\n",
    "file_list = get_list_files(my_dir,'png')\n",
    "\n",
    "X_extra = np.zeros((len(file_list),IMG_HEIGHT,IMG_WIDTH,3),dtype='uint8')\n",
    "\n",
    "for idx,file in enumerate(file_list):\n",
    "    img = cv2.imread(my_dir+'/'+file)\n",
    "    img = cv2.resize(img,(32,32))\n",
    "    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
    "    X_extra[idx] = img\n",
    "\n",
    "print('Extra dataset size:{}|Datatype:{}'.format(X_extra.shape,X_extra.dtype))\n",
    "#Data pre-processing\n",
    "X_extra_prep = preprocessed(X_extra).reshape(-1,IMG_HEIGHT,IMG_WIDTH,1)\n",
    "print('Preprocessed Extra dataset size:{}|Dtatype:{}'.format(X_extra_prep.shape,X_extra_prep.dtype))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original (left) and pre-processed(right) iamges\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x576 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize images:original(left) and after pre-processing(right)\n",
    "\n",
    "#initialize subplots\n",
    "_,ax = plt.subplots(len(file_list),2,figsize=(4,8))\n",
    "col_plot = 0\n",
    "print('Original (left) and pre-processed(right) iamges')\n",
    "for i in range(len(X_extra)):\n",
    "    img = X_extra[i]\n",
    "    ax[i,col_plot].imshow(img)\n",
    "    ax[i,col_plot].annotate(file_list[i],xy=(31,5),color='black',fontsize='10')\n",
    "    ax[i,col_plot].axis('off')\n",
    "    col_plot +=1\n",
    "    ax[i,col_plot].imshow(X_extra_prep[i,:,:,0],cmap='gray')\n",
    "    ax[i,col_plot].axis('off')\n",
    "    col_plot = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./traffic_modelora\n",
      "Prediction on extra data\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##inference\n",
    "feed_dict = {x:X_extra_prep,keep_prob:1,k_p_conv:1}\n",
    "k_top = 5\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    loader = tf.train.import_meta_graph('traffic_model'+model_nbr+'.meta')\n",
    "    loader.restore(sess,tf.train.latest_checkpoint('./'))\n",
    "    pred_proba = sess.run(softmax_operation,feed_dict=feed_dict)\n",
    "    prediction = np.argmax(pred_proba,1)\n",
    "    # top 5 probabilities\n",
    "    top_k_values = tf.nn.top_k(softmax_operation,k_top)\n",
    "    top_k_proba = sess.run([softmax_operation,top_k_values],feed_dict=feed_dict)\n",
    "\n",
    "#Visualize image with predicted label\n",
    "print('Prediction on extra data')\n",
    "for i in range(len(X_extra)):\n",
    "    plt.figure(figsize=(1,1))\n",
    "    img = X_extra[i]\n",
    "    plt.imshow(img)\n",
    "    plt.title(sign_names[prediction[i]][1],fontsize=10)\n",
    "    plt.axis('off')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看的出来上面的预测结果有的是不正确的，这与我们前面模型训练有关。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x576 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###TOP 5 Probabilities \n",
    "_,ax = plt.subplots(len(file_list),2,figsize=(4,8))\n",
    "col_plot = 0\n",
    "for i in range(len(X_extra)):\n",
    "    img = X_extra[i]\n",
    "    ax[i,col_plot].imshow(img)\n",
    "    ax[i,col_plot].axis('off')\n",
    "    col_plot +=1\n",
    "    ax[i,col_plot].barh(-np.arange(k_top),top_k_proba[1][0][i],align='center')\n",
    "    \n",
    "    #annotation\n",
    "    for k in range(k_top):\n",
    "        text_pos = [top_k_proba[1][0][i][k]+.1,-(k+0.4)]\n",
    "        ax[i,col_plot].text(text_pos[0],text_pos[1],sign_names[top_k_proba[1][1][i][k]][1],fontsize=8)\n",
    "    ax[i,col_plot].axis('off')\n",
    "    col_plot = 0\n",
    "plt.show()                                                     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-Scale Convolutional Networks\n",
    "前面我们采用的网络结构是传统经典的卷积网络结构,接下来我们实现一个multi-scaled network from lecun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#weights\n",
    "weights ={  \n",
    "    'W_conv1': weight_variable([3, 3, IMG_DEPTH, 80], mean=mu, stddev=sigma, name='W_conv1'),\n",
    "    'W_conv2': weight_variable([3, 3, 80, 120], mean=mu, stddev=sigma, name='W_conv2'),\n",
    "    'W_conv3': weight_variable([4, 4, 120, 180], mean=mu, stddev=sigma, name='W_conv3'),\n",
    "    'W_conv4': weight_variable([3, 3, 180, 200], mean=mu, stddev=sigma, name='W_conv4'),\n",
    "    'W_conv5': weight_variable([3, 3, 200, 200], mean=mu, stddev=sigma, name='W_conv5'),\n",
    "    'W_fc1': weight_variable([8000, 80], mean=mu, stddev=sigma, name='W_fc1'),\n",
    "    'W_fc2': weight_variable([80, 80], mean=mu, stddev=sigma, name='W_fc2'),\n",
    "    'W_fc3': weight_variable([80, 43], mean=mu, stddev=sigma, name='W_fc3'),\n",
    "}\n",
    "def traffic_model_Lecun(x,keep_prob,keep_p_conv,weights,biases):\n",
    "    '''\n",
    "    ConvNet model for Traffic sign classifier\n",
    "    x - input image is tensor of shape(n_imgs,img_height,img_width,img_depth)\n",
    "    keep_prob - hyper parameter of the dropout operation\n",
    "    weights - dictionary of the weights for convolution layers and fully connected layers\n",
    "    biases dictionary of the biases for convolutional layers and fully connected layers\n",
    "    '''\n",
    "    # Convolutional block 1\n",
    "    conv1 = conv2d(x, weights['W_conv1'], strides=[1,1,1,1], padding='VALID', name='conv1_op')\n",
    "    conv1_act = tf.nn.relu(conv1 + biases['b_conv1'], name='conv1_act')\n",
    "    conv1_drop = tf.nn.dropout(conv1_act, keep_prob=k_p_conv, name='conv1_drop')\n",
    "    conv2 = conv2d(conv1_drop, weights['W_conv2'], strides=[1,1,1,1], padding='SAME', name='conv2_op')\n",
    "    conv2_act = tf.nn.relu(conv2 + biases['b_conv2'], name='conv2_act')\n",
    "    conv2_pool = max_2x2_pool(conv2_act, padding='VALID', name='conv2_pool')\n",
    "    pool2_drop = tf.nn.dropout(conv2_pool, keep_prob=k_p_conv, name='conv2_drop')\n",
    "    \n",
    "    #Convolution block 2\n",
    "    conv3 = conv2d(pool2_drop, weights['W_conv3'], strides=[1,1,1,1], padding='VALID', name='conv3_op')\n",
    "    conv3_act = tf.nn.relu(conv3 + biases['b_conv3'], name='conv3_act')\n",
    "    conv3_drop = tf.nn.dropout(conv3_act, keep_prob=k_p_conv, name='conv3_drop')\n",
    "    conv4 = conv2d(conv3_drop, weights['W_conv4'], strides=[1,1,1,1], padding='SAME', name='conv4_op')\n",
    "    conv4_act = tf.nn.relu(conv4 + biases['b_conv4'], name='conv4_act')\n",
    "    conv4_pool = max_2x2_pool(conv4_act, padding='VALID', name='conv4_pool')\n",
    "    conv4_drop = tf.nn.dropout(conv4_pool, keep_prob, name='conv4_drop')\n",
    "    #Convolution block 3\n",
    "    conv5 = conv2d(conv4_drop, weights['W_conv5'], strides=[1,1,1,1], padding='VALID', name='conv5_op')\n",
    "    conv5_act = tf.nn.relu(conv5 + biases['b_conv5'], name='conv5_act')\n",
    "    conv5_pool = max_2x2_pool(conv5_act, padding='VALID', name='conv5_pool')\n",
    "    conv5_drop = tf.nn.dropout(conv5_pool, keep_prob, name='conv5_drop')\n",
    "    # Flatten the out put convolution block 2\n",
    "    fc_ = flatten(conv4_drop)\n",
    "    #Fully connected layers\n",
    "    fc0 = flatten(conv5_drop)\n",
    "    fc = tf.concat([fc_,fc0],1)\n",
    "    print('fc shape:',fc.get_shape())\n",
    "    fc1 = tf.nn.relu( tf.matmul( fc, weights['W_fc1'] ) + biases['b_fc1'], name='fc1' )\n",
    "    fc1_drop = tf.nn.dropout(fc1, keep_prob, name='fc1_drop')\n",
    "    fc2 = tf.nn.relu( tf.matmul( fc1_drop, weights['W_fc2'] ) + biases['b_fc2'], name='fc2' )\n",
    "    fc2_drop = tf.nn.dropout(fc2, keep_prob, name='fc2_drop')\n",
    "    logits = tf.add(tf.matmul(fc2_drop, weights['W_fc3']),biases['b_fc3'], name='logits')  \n",
    "    \n",
    "    return [weights, logits]"
   ]
  }
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